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Ernestine Dickhaut

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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.

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The goals of design science research (DSR) projects are to generate novel and useful artifacts and to produce rigorous and generalizable design knowledge. Often, DSR projects are conducted in collaborative, interdisciplinary project teams. Different disciplinary approaches to codifying design knowledge result in challenging project interactions. To study this situation, we analyze design knowledge codification in interdisciplinary teams over time. We gain insights from a survey of recent DSR papers that have been published in the AIS Senior Scholars’ Basket. We then present a detailed case study of a longitudinal project that brought to light issues of sharing design knowledge across disciplinary borders. Drawing from the survey and case study, we provide actionable guidance on how to effectively codify and share design knowledge to support researchers and practitioners to build useful artifacts and to make interdisciplinary design knowledge contributions reusable and applicable.

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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.

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Novel technologies such as smart personal assistants integrate digital services into everyday life. These services use personal data to offer personalized services. While they are subject to special data protection regulations at the time of development, there are few guidelines describing the transition from legal requirements to implementation. To reduce risks, services depend on external legal assessments. With developers and legal experts often missing either legal or technical knowledge, the challenge lies in bridging this gap. We observe that design patterns support both developers and legal experts, and we present an approach in which design patterns are leveraged to provide twofold value for both developers and legal experts when dealing with novel technologies. We conducted a revelatory case study for smart personal assistants and scaffolded the case interpretation through cognitive fit theory. On the basis of the findings, we develop a theoretical model to explain and predict the twofold value of design patterns to develop and assess lawful technologies.

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Durch die Digitalisierung werden immer mehr Technologien entwickelt. Dabei gewinnt die soziotechnische Systementwicklung zunehmend an Bedeutung, in deren Rahmen nicht nur das technische System isoliert betrachtet wird, sondern auch der Nutzer und sein Umfeld. Insbesondere bei der Entwicklung rechtsverträglicher Systeme stehen Entwickler häufig aufgrund fehlenden rechtlichen Fachwissens vor großen Herausforderungen. Dies gilt insbesondere dann, wenn es um intelligente, selbstlernende Systeme geht. Diese Systeme sammeln, um die Qualität ihrer Dienste zu optimieren und Nutzerbedürfnissen zu entsprechen mithilfe leistungsfähiger Technologien große Mengen an personenbezogenen Daten, was Risiken für die informationelle Selbstbestimmung der Nutzer mit sich bringt. Um diesen Risiken entgegenzuwirken nutzen wir Anforderungs- und Entwurfsmuster. Ziel des Beitrags ist daher mittels eines multi-methodischen Ansatzes aufzuzeigen, welchen Beitrag interdisziplinäre Anforderungs- und Entwurfsmuster für die Entwicklung rechtsverträglicher und qualitativ hochwertiger KI-basierter Systeme leisten können. Um die Wirksamkeit der Muster zu untersuchen wurde mithilfe der Muster ein Lernassistent entwickelt und durch die Methode der Simulationsstudie evaluiert.

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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.

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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.

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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.

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The goal of design science research is the generation of novel artifacts. Thereby DSR projects generate valuable design knowledge, thus, underscoring the importance to codify of design knowledge for achieving scientific progress. The research community observes that DSR projects generate a large amount of design knowledge, but the developed knowledge often ends as a single success story. To counter this situation, we analyze the variety of design knowledge representation forms that have been published in the AIS Senior Scholars’ Basket in design science research papers. Based on our systematic literature review, we identify prevalent ways of design knowledge representations. We provide as a central contribution how to effectively communicate design knowledge through the derivation of recommendations that provides practical guidance to support researchers and practitioners in making design knowledge contributions reusable and applicable.

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get_appTorben Jan Barev, Ernestine Dickhaut, Sabrina Schomberg, Andreas Janson, Sofia Schöbel, Thomas Grote, Gerrit Hornung, Jan Marco Leimeister
Buch
Digitale Arbeitsumgebungen sind heutzutage allgegenwärtig. Zugleich hat die Anzahl an Möglichkeiten, Informationen über Arbeitstätigkeiten sowie sensible persönliche Daten elektronisch zu erfassen, drastisch zugenommen (Backhaus 2019). Unternehmen nutzen immer mehr Formen von digitalen Arbeitssystemen und setzen fortschrittliche Instrumente wie Big Data Analytik oder Künstliche Intelligenz ein. Damit können Daten schneller und in größerem Umfang als je zuvor gesammelt, aggregiert und analysiert werden (Malhotra et al. 2004). Darüber hinaus können Daten gesammelt werden, ohne dass der Einzelne davon weiß (Bélanger and Crossler 2011). Auf der einen Seite birgt die weit verbreitete Analyse von persönlichen Daten ein erhebliches Innovationspotenzial, einen wirtschaftlichen Wert sowie effizientere Arbeitsmodelle (Erevelles et al. 2016). Auf der anderen Seite ist die Anfälligkeit für Diskriminierung, kommerzielle Ausbeutung und unerwünschte Überwachung ebenso allgegenwärtig. Dadurch wird die Akzeptanz moderner IT‐Systeme behindert. Dieser Interessenkonflikt wird sich in den kommenden Jahren vermutlich noch verschärfen, da die Unternehmen von der fortschreitenden Digitalisierung profitieren.

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