Browsing by Author "Dickhaut, Ernestine"

Artificial intelligence (AI) possesses the potential to augment customer service employees e.g. via decision support or solution recommendations. Still, its underlying data for training and testing the AI systems is provided by human annotators through human-in-the-loop configurations. However, due to the high effort for annotators and lack of incentives, AI systems face low underlying data quality. That in turn results in low prediction performance and limited acceptance by the targeted user group. Faced with the enormous volume and increasing complexity of service requests, IT service management (ITSM) especially, relies on high data quality for AI systems and in-corporating domain-specific knowledge. By analyzing the existing labeling process in that specific case, we design a revised to-be process and develop a conceptual model from a value co-creation perspective. Finally, a functional prototype as an instantiation in the ITSM domain is implemented and evaluated through accuracy metrics and user evaluation. The results show that the new process increases the perceived value of both labeling quality and the perceived prediction quality. Thus, we contribute a conceptual model that supports the systematic design of efficient and interactive labeling processes in diverse applications of reinforcement learning systems.

Although the idea of low code development is not new, the market for these oftentimes platform-based development approaches is exponentially growing. Especially factors such as increasing affinity for technology development across all user groups, consumerization of development, and advancing digitalization are opening a new target group for the low code movement. The broad application possibilities of low code, as well as the benefits, are therefore getting more important for businesses. Especially for small and medium-sized enterprises (SMEs), low code constitutes a promising avenue to survive and succeed in the rapidly changing world. However, a clear understanding regarding the application of this paradigm of software development in SMEs is still missing. To provide a coherent understanding of the phenomenon low code in SMEs, we review extant literature and conduct interviews, identifying potential application domains and conceptualizing the benefits and challenges of low code from a holistic perspective.

Smart personal assistants (SPAs) are proliferating into our daily lives and are a ubiquitous platform for providing digital services. However, when de-signing such innovative IT artifacts, interdisciplinary domain knowledge is of-ten needed. For example, SPAs utilize a plethora of sensors and cloud-based computation of data to deliver high-quality services, but those services, for ex-ample, may conflict with current regulations of law, e.g., with the Data Protec-tion Regulation (GDPR) in Europe. In that sense, approaches are needed to overcome the limited domain knowledge of developers. Thus, we propose in our study a pattern-based approach to codify interdisciplinary design knowledge. For this purpose, we derive theory-motivated requirements, develop design principles for patterns, and evaluate the utility of the patterns. Our re-sults from a 2x2 fully randomized field study show that the provision of pat-terns for SPA development supports interdisciplinary design through better con-sideration of both service quality and law compatibility. Thus, we provide de-sign contributions concerning how we effectively codify and communicate de-sign knowledge and provide practical guidance for supporting interdisciplinary IS development.

Design science projects are of great interest in information systems (IS) research. Typically, design-oriented projects generate valuable design knowledge through the design and possible instantiation of artifacts. Although designing novel artifacts and accumulating design knowledge is common practice in IS, there is still limited shared knowledge about the distinctive characteristics of design knowledge to facilitate its accumulation. To address this issue, we develop a design knowledge taxonomy and contribute to a deeper understanding of design knowledge properties. The taxonomy is grounded on a systematic literature review, followed by a combination of empirical-to-conceptual and conceptual-to-empirical iterations. We evaluate the taxonomy by interviewing six domain experts and demonstrate its practical application and utility. Thus, the taxonomy consists of key dimensions and characteristics of design knowledge and contributes to a better scientific understanding of its characteristics. Practitioners can use the taxonomy as an instrument to further understand, design, and accumulate design knowledge.

Designing novel technologies provide challenges to developers. To support developers in designing these technologies, design knowledge must be codified and made applicable for the future. In systems development, design patterns provide proven solutions to solving recurring problems. They contain templates for describing design information, often in tabular form, and are established tools for making complex knowledge accessible and applicable. Design patterns play a critical role in both practice and research in finding potential solutions. For researchers, patterns can provide a method for codifying design knowledge for future research. For practitioners, design patterns provide established solutions to recurring problems. By applying them in a particular context, the pattern represents elements of both the problem-space and the solution-space, providing an opportunity to bridge the gap between the two spaces. Due to the abstraction of design patterns, they can be used for different application scenarios. The preparation of the design knowledge in the design pattern is a critical step to support the user in the best possible way, that determines the usefulness of the pattern.

To ensure that an intended target group accepts and uses an information system (IS) is a major challenge for service systems engineering and a key interest in IS research. On the one hand, individuals’ cultural values affect their willingness to use an IS; on the other hand, information technology (IT) is neither value-neutral and, thus, also affects IT acceptance and usage. Therefore, the adaptation of IS should consider both sources of value. Thus, in this paper, we present the theory-driven design of a method for culture-sensitive IS adaptation that draws on IT-culture conflict theory. Our two-fold evaluation approach results show that the method enables to create feasible results for developing culture-sensitive design solutions for IS. As a theoretical contribution, we contribute to the exploration of culture in IS development; as a practical contribution, we provide guidance in how to adapt IS for specific target groups.

Higher legal standards regarding the data protection of individuals, such as the European General Data Protection Regulation, increase the pressure on developing lawful systems. In the development of technologies, not only developers are involved. It also requires knowledge from other stakeholders, such as legal experts, that lack technical knowledge but are required to understand IT artifacts. We see two strings that can benefit from the use of design patterns: first, the well-known use of design patterns to support developers in case of recurring problems. Second, we see potential that legal experts, who have to interact with and understand complicated, novel technologies, benefit from the same patterns. We conduct a revelatory case study using design patterns to develop and assess a smart learning assistant. We scaffolded the case interpretation through the human-centered view of socio-materiality and provide contributions concerning the use of design patterns in the development and assessment of lawful technologies.

AI-based recommendation systems to augment working conditions in the field of IT service management (ITSM) have attracted new attention. However, many IT support organizations possess high volumes of tickets but are confronted with low quality, to which they train the underlying models of their AI systems. In particular, support tickets are documented insufficiently due to time pressure and lack of motivation. Following design science research, we design and evaluate an analytics pipeline to address the data quality issue. The pipeline can be applied to assess and extract high-quality support tickets for subsequent model training and operation. Based on a data set of 60.000 real-life support tickets from a manufacturing company, we develop the artifact, instantiate a recommender system and achieve a higher pre-diction performance in comparison to naïve enrichment methods. In terms of data management literature, we contribute to the understanding of as-sessing textual ticket data quality. By deriving a pipeline reference model, we move towards a general approach to designing machine learning-driven data quality analytics pipelines for attached recommender systems.

The use of learning videos has grown dramatically in the last decades. However, there is also increasing criticism against the use of learning videos concerning the harmful effects on the learning process, such as a decrease in motivation, engagement, and learning success. Although interactive learning videos can be helpful in supporting students' learning process, the design of such videos is still challenging for instructional designers. To overcome these challenges, we follow a Design Science Research (DSR) approach and develop a design pattern-based process to support the development of interactive learning videos. To this end, we engage in multiple DSR cycles where we conduct a systematic literature review, collect practical and theoretical requirements, as well as develop and evaluate our artifact in two cycles. Finally, we codify our results into design patterns and a creation process for interactive learning videos. With our contribution, we make design knowledge for such learning materials accessible by including tangible and applicable practices during our interactive learning video creation process. Our contribution aims to support developers and educators in designing more effective learning videos.

The COVID-19 pandemic has brought about major changes in digitization in many areas of life and professions. New areas were digitized almost overnight, the school system in Germany was no exception leading to a demand for videoconferencing tools and communication platforms. These technologies have many different functionalities that need to be discovered, explored, and exploited by the user. Given the disruptive events that the COVID pandemic brought to us, this paper aims to shed light on how the dynamics of discovery, exploration, and exploitation unfolds. We use a functional affordance theory perspective to analyze and understand how user learn to use new technologies. To do this, we conducted an exploratory case-study-based research design including interviews with teachers from various schools to analyze how they appropriate new technologies to develop an explanatory theoretical model.

Design Thinking has become a well-established approach to solving wicked problems through creative and conceptual solutions. Thus, Design Thinking approaches usually end with finding novel solutions but do not offer support in the practical implementation of these solutions. To bridge the gap between finding creative solutions and implementing them into suitable end products, we see Design Patterns as a useful approach to combine the advantages of both sides—the human-centered Design Thinking approach and the practical implementation in system development. Design Patterns offer proven solutions to recurring problems and thus provide design knowledge to solve complex design problems. In this contribution, we demonstrate how Design Patterns act as a complement to Design Thinking by using the example of designing a lawful smart personal learning assistant. We use Design Thinking to extract the design solution of the lawful learning assistant and develop it with the help of Design Patterns. We demonstrate the use of Design Patterns by using the deletion routine Design Pattern as an example of how a Design Pattern can be used for lawful design in addition to their known fields of application in system development. The evaluation results show that the combination of Design Thinking and Design Patterns lead to an approach that not only identifies novel, complex solutions but also supports their practical implementation.

Higher legal standards with regards to data protection of individuals such as EU-GDPR are increasing the pressure on developers of IT artifacts. This is especially prevalent when considering conversational speech agents (CSA) that are collecting data in new ways, and thus, are oftentimes producing conflicts with existing law regulations. For this purpose, we introduce the law simulation method which is a well-known evaluation method among law researchers for capturing legal compatibility of IT artifacts such as CSA. With this rigorous method, we are able to derive actionable guidance for CSA developers to evaluate developer efforts for increasing legal compatibility. To illustrate our methodological approach, we describe in this workshop paper key steps of the method with respect to the evaluation of CSA. We discuss how this can serve as the foundation for a new evaluation method of legally compatible systems in information systems.