Publications Overview

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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Escalation of Commitment (EoC) - the tendency to persist with failing courses of action - can determine whether a distressed Information Systems (IS) project can be turned around. To disentangle the emotional and cognitive factors that give rise to EoC we conducted a between-subject randomized controlled laboratory experiment with 75 Master, MBA, and Ph.D. students including data triangulation between neurophysiological and behavioral measures. This study successfully replicates the EoC bias in the context of IS project distress, provides evidence for a psychophysiological link, supports the predictions on the role of negative and complex emotional states of self-justification theory over coping theory, and adds to a better understanding of how escalation tendency changes over time due to learning effects. Our findings contribute to enhancing decision-making in uncertain environments by using cognitive and emotional markers and thereby provide the foundation for developing neuro-adaptive de-escalation strategies.

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2B-Innovationsplattformen erfreuen sich zunehmender Beliebtheit in der Wertschöpfung von Unternehmen. Solche Innovationsplattformen erleichtern das Innovieren zwischen mehreren Partnern, die sogenannte Multipartner-Innovation (MPI). MPI ist ein komplexer Prozess mit verschiedenen Herausforderungen. B2B-Innovationsplattformen versprechen diese komplexitätsbedingten Herausforderungen durch ihre Hebelwirkungen zu adressieren und MPI-Ergebnisse zu fördern. Daher gelten B2B-Innovationsplattformen als vielversprechende Organisationslogik im MPI-Kontext. Die genaueren Umstände, unter denen B2B-Plattformhebel die Komplexitätsherausforderungen von MPI-Prozessen in Business-Ökosystemen bewältigen, sind jedoch noch nicht definiert. Ziel dieser Dissertation ist es daher, die Rolle von B2B-Plattformhebeln bei der Bewältigung von Komplexitätsherausforderungen zu untersuchen und zu erklären, ob und wie Plattformen tatsächlich eine vielversprechende Organisationslogik darstellen.Die Komplexitätswissenschaft dient dabei als theoretische Grundlage um aufzuzeigen, wie Komplexität entsteht und zu Herausforderungen in Business-Ökosystemen führt. Anschließend wird die agentenbasierte Modellierung als Forschungsmethode eingesetzt, um die zentralen Wechselbeziehungen und die resultierende Komplexität zu modellieren, Simulationsexperimente durchzuführen, und neue theoretische Erkenntnisse zu gewinnen. Das entwickelte Simulationsmodell und die abgeleiteten Erkenntnisse werden in den einzelnen Beiträgen der Dissertation iterativ verfeinert. Das Ergebnis ist ein erweiterbares agentenbasiertes Modell einer B2B-Innovationsplattform in einem komplexen Business-Ökosystem sowie eine Reihe theoretischer Erkenntnisse. Aus Forschungssicht wird gezeigt, dass die Komplexität eines Business-Ökosystems die Fähigkeit von B2B-Plattformhebeln, Komplexitätsherausforderungen zu bewältigen, massgeblich beeinflusst. MPI-Komplexität erzeugt verschiedene Komplexitätsregionen im Business-Ökosystem. B2B-Plattformhebel können die Komplexitätsprobleme nur in der Region der emergenten Komplexität bewältigen. Für Praktiker impliziert dies, dass B2B-Innovationsplattformen nicht in allen Branchen gleichermaßen disruptiv sind. Die Komplexität auf Ökosystemebene sollte demnach bei der Frage berücksichtigt werden, ob eine Plattformorganisationslogik tatsächlich sinnvoll ist.

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Sprachbasierte Interfaces (Conversational Interfaces, CIs) verändern die Art und Weise, wie Nutzer mit Unternehmen in einem breiten Spektrum von Dienstleistungen interagieren. CIs ermöglichen den Unternehmen, auf der Grundlage der derzeitigen kontinuierlichen Fortschritte in der künstlichen Intelligenz (KI) und der Verarbeitung natürlicher Sprache (NLP), die Kundeninteraktion zu automatisieren, ohne dass die Dynamik menschlicher Interaktion verloren geht. Somit erzeugen CIs eine Dienstleistungserfahrung, die der Interaktion mit einem menschlichen Servicemitarbeiter ähnelt. Von der Unterstützung bei der Anmeldung von Versicherungsansprüchen bis hin zur Bereitstellung von Produktempfehlungen unterstützen CIs die Nutzer zunehmend bei der Lösung von Serviceanfragen. Sie sind dabei intuitiver und natürlicher als herkömmliche Servicetechnologien (z. B. Websites). Einem neuartigen Interaktionsparadigma folgend ahmen CIs zwischenmenschliche Kommunikation in schriftlicher oder gesprochener Form nach. Als Folge können CIs mit den Nutzern auf eine Art und Weise interagieren, die bisher als Wesensmerkmal menschlicher Kommunikation galt: ein auf natürlicher Sprache basierender Dialog. Dies wirft grundlegende Fragen darüber auf, wie Nutzer auf CI-gestützte Service-Interaktionen reagieren und welche Auswirkungen dies auf das Serviceergebnis haben könnte. Der übergreifende Beitrag dieser Dissertation ist ein neuartiger konzeptioneller Rahmen für CI-unterstützte Service-Interaktionen, der auf grundlegenden Theorien der menschlichen Kommunikation, der technologievermittelten Servicebereitstellung und auf laufenden Arbeiten zu Mensch-KI-Beziehungen aufbaut und diese integriert. Der konzeptionelle Rahmen wird in sechs komplementären Projekten entwickelt und getestet. In Kombination zeigen diese Arbeiten, wie CIs und ihr Design (z.B. das Geschlecht der Stimme, die Interaktionsmodalität) die Nutzererfahrungen (z.B. Vertrauen, Flow, Fluency) prägen und sich letztlich auf das Ergebnis der Dienstleistung auswirken. Darüber hinaus wird untersucht, wie moderierende Faktoren, die sich in der konversationalen Gestaltung von CIs manifestieren, diese Effekte beeinflussen. Die Dissertation hat wichtige Implikationen für Forschung und Praxis: Aus theoretischer Sicht zeigt diese Arbeit, wie grundlegende Erkenntnisse aus der menschlichen Kommunikation genutzt werden können, um die aufkommende interdisziplinäre Forschung zu dem Thema voranzutreiben, wie Nutzer in Beziehung zu den Service-Interaktionen von CIs treten und von diesen beeinflusst werden. Die Ergebnisse dieser Dissertation haben ebenfalls wichtige praktische Auswirkungen für Unternehmen, die das Potenzial von CIs zur Verbesserung der Nutzererfahrungen und das theoriegeleiteten Konversationsdesign von CIs nutzen wollen.

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Enterprise architecture management (EAM) is commonly employed by large organizations to coordinate local information system development efforts in line with organization-wide strategic objectives while simultaneously avoiding redundancies and inconsistencies. Even though EAM tools and processes have become increasingly mature over the past decade, many organizations still struggle to generate impact from their EAM initiatives. To this end, we describe how enterprise architects at Commerzbank, a major international bank, employed a control mechanism portfolio perspective to more effectively anchor EAM within the organization. This approach allows to purposefully combine a wide range of different formal and informal EAM control mechanisms, thereby going beyond the formal, topdown driven mechanisms predominantly discussed in EAM literature. Furthermore, such EAM control mechanism portfolios provide an effective means to purposefully realign EAM in reaction to major strategic shifts. The application of this perspective is demonstrated by tracing the evolution of EAM at Commerzbank for more than a decade (2008 to 2018) through a turbulent and challenging competitive environment, resulting in several major strategic realignments that required corresponding adjustments in EAM. We believe that such consciously designed and diversified EAM control mechanism portfolios also provide a useful means for other large organizations to more effectively conduct EAM.

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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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With this paper, we examine the use of data analytics for crisis management in automotive procurement departments. Possible business values of data analytics were part of numerous research approaches. Nevertheless, automotive manufacturers are repeatedly confronted with supply chain disruptions. Procurement departments have a central role within supply chains and are predominantly responsible for stable supply processes. Taking into account the potential of data analytics, such crises should be avoided or at least mitigated. Thus, there is the question, why data analytics cannot currently help automotive procurement departments by facing such crises. We therefore evaluate problems and obstacles by implementing and using data analytics in automotive procurement departments. Therefore, we talk to experienced procurement experts for evaluating practical insights. With our findings we provide practical insights and applicable recommendations for action with the goal of helping procurement leaders to better leverage data analytics for meeting current and future crises.

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Pandemics like COVID-19 highlight the needs and pitfalls of inclusive and equitable education in a digital society. IT-based instructional designs are needed to increase learners’ expertise, and to develop higher-order thinking skills. Instructional designs for collaborative learning (CL) seem to be a promising solution. However, they are mostly suitable for face-to-face and not for distance teaching. The core problem that impedes their reusability and scalability is a ‘collaboration problem’ for which collaboration engineering (CE) provides guidance. Therefore, we deploy a design science research study and contribute to CL and CE literature. We develop requirements and provide the design of an IT-based collaborative work practice fostering CL. We provide empirical evidence with an online experiment in a large-scale lecture with undergraduate business information students. This reveals that groups of learners who followed our CL experience achieve higher levels of expertise than those who followed a traditional ad hoc CL experience.

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Collaborative work practices (CWPs) package facilitation expertise and have the potential to increase team productivity up to 90%. Collaboration engineers develop CWPs and deploy them to practitioners that execute them. These CWPs, however, are typically customized to conditions of a specific use case. This creates the challenge that changing use case conditions or even small variations across contexts, hinder well-performing CWPs of being applied more often to create a long-term value. Practitioners fail to adapt existing CWPs due to missing collaboration expertise and adaptation guidelines. To address this challenge in collaboration engineering literature, we introduce a) the Subject Matter Expert role; b) the ‘CWP Adaptation Approach’ that formalizes the transfer of CWPs to different contexts with parameterized Templates and Guidebooks. To show a first proof-of-concept, we further inductively generalize from an exemplarily use case with a well-performing CWP in the educational domain.

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Some classes of person-oriented services such as healthcare services require individualization to be effective. Individualizing services and corresponding patient pathways are costly. To provide such services in an individualized, but also efficient manner, service modularization is known as a solution. Until now, modularization parameters that take healthcare specificities into account are missing. This paper closes this gap. Following a design science research approach, we iteratively build and evaluate a set of healthcare-specific modularization parameters. For requirements elicitation, refinement of the modularization parameters and their evaluation, we conduct interviews with domain experts from patient pathways in oncology care as well as with service design and business development experts. As main theoretical contribution, this paper provides design knowledge for the modularization of healthcare services. For practice, the set of parameters assists healthcare providers in the efficient provision of individualized, patient-centric solutions and patient pathways.

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Onboarding has always emphasized personal contact with new employees. Excellent onboarding can extend employee retention and improve loyalty. Even in a physical setting, the onboarding process is demanding for both the newcomer and the onboarding organization. Remote work, in contrast, has made this process even more challenging by forcing a rapid shift from offline to online onboarding practices. Organizations are adopting new technologies like artificial intelligence (AI) to support work processes, such as hiring processes or innovation facilitation, which could shape a new era of work practices. However, it has not been studied how AI applications can or should support onboarding. Therefore, our research conducts a literature review on current onboarding practices and uses expert interviews to evaluate AI's potential and pitfalls for each action. We contribute to the literature by presenting a holistic picture of onboarding practices and assessing potential application areas of AI in the onboarding process.

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Organizations claim to host what is called a metaverse – an extended version of our real world. First rudimental realizations of such metaverses can be found throughout the internet, e.g., Epic Game’s Fortnite. At the same time, research and practice struggle to specify what a metaverse truly is and how we can characterize it. With our work, we analyze the proximity of the realization of a holistic metaverse platform and present the results of a qualitative interview study (n=30). The goal of our work is to use the expertise of practitioners to discuss different examples that claim to represent a metaverse, e.g., Second Life and Decentraland. To achieve this goal, we develop a typology we call the Metagon and use it to evaluate existing metaverse platforms. We contribute to theory by clarifying the meaning of metaverse platforms. Practitioners are guided by a demonstration of metaverse characteristics.

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Human-agent interaction is increasingly influencing our personal and work lives through the proliferation of conversational agents in various domains. As such, these agents combine intuitive natural language interactions by also delivering personalization through artificial intelligence capabilities. However, research on CAs as well as practical failures indicate that CA interaction oftentimes fails miserably. To reduce these failures, this paper introduces the concept of building common ground for more successful human-agent interactions. Based on a systematic review our analysis reveals five mechanisms for achieving common ground: (1) Embodiment, (2) Social Features, (3) Joint Action, (4) Knowledge Base, and (5) Mental Model of Conversational Agents. On this basis, we offer insights into grounding mechanisms and highlight the potentials when considering common ground in different human-agent interaction processes. Consequently, we secure further understanding and deeper insights of possible mechanisms of common ground in human-agent interaction in the future.

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The endless stream of social media newsfeeds and stories captivates users for hours on end, sometimes exceeding what users themselves consider unhealthy. However, reducing one's social media consumption has proven to be challenging. To address this issue, this study investigates how the co-creation of the digital feedback nudge can improve digital well-being without increasing privacy threats. To achieve this goal, a mixed method study is used through a two-week pre-post study design. Results demonstrate that co-creation significantly increased users' sense of agency, sense of accomplishment and perceived sense of privacy while reducing users' privacy concern. Furthermore, the feedback nudge allowed participants to significantly decrease their social media use.

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Significant investments in information systems (IS) over the past decades have led to increasingly complex IS architectures in organizations, which are difficult to understand, operate, and maintain. We investigate this development and associated challenges through a conceptual model that distinguishes four constituent elements of IS architecture complexity by differentiating technological from organizational aspects and structural from dynamic aspects. Building on this conceptualization, we hypothesize relations between these four IS architecture complexity constructs and investigate their impact on architectural outcomes (i.e. efficiency, flexibility, transparency, and predictability). Using survey data from 249 IS managers, we test our model through a partial least squares (PLS) approach to structural equation modelling (SEM). We find that organizational complexity drives technological complexity and that structural complexity drives dynamic complexity. We also demonstrate that increasing IS architecture complexity has a significant negative impact on efficiency, flexibility, transparency, and predictability. Finally, we show that enterprise architecture management (EAM) helps to offset these negative effects by acting as a moderator in the relation between organizational and technological IS architecture complexity. Thus, organizations without adequate EAM are likely to face large increases in technological complexity due to increasing organizational complexity, whereas organizations with adequate EAM exhibit no such relation.

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What stories are told in national artificial intelligence (AI) policies? Combining the novel technique of structural topic modeling (STM) and qualitative narrative analysis, this paper examines the policy narratives in 33 countries’ AI policies. We uncover six common narratives that are dominating the political agenda concerning AI. Our findings show that the policy narratives' saliences vary across time and countries. We make several contributions. First, our narratives describe well-grounded, supportable conceptions of AI among governments, and show that AI is still a fairly novel, multilayered, and controversial phenomenon. Building on the premise that human sensemaking is best represented and supported by narration, we address the applied rhetoric of governments to either minimize the risks or exalt the opportunities of AI. Second, we uncover the four prominent roles governments seek to take concerning AI implementation: enabler, leader, regulator, and/or user. Third, we make a methodological contribution toward data-driven, computationally-intensive theory development. Our methodological approach and the identified narratives present key starting points for further research.

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The interest in digital identities has increased considerably in academia and practice in recent years. This can be seen by the many electronic identity projects worldwide and the numerous published studies that provide insightful narratives and descriptive case findings about success factors and barriers to the adoption of national authentication infrastructures. In this paper, we take a closer look to the role of trust on the design and implementation of a nation-wide e-credential market. We argue that trust in political and economic institutions can be an important factor to explain differences in the chosen cooperative arrangement which can range from monopolistic, purely state-controlled e-credential markets, to polypolistic, decentralized e-credential markets where also private vendors offer state recognized e-ID on their own or in partnership with the government. Following an inductive reasoning process, we develop three testable propositions which may inspire further empirical research and offer practitioners a new angle to rethink e-credential markets in the light of citizen trust in political and economic institutions.

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Over recent decades, many platform-native start-ups and firms were founded and some are now among the world’s most valuable. This study, however, focuses on an incumbent firm transitioning from a long established product platform ecosystem to an innovation platform ecosystem in response to the platform-natives’ threats of disruption. We specifically investigate the dynamic capabilities needed by the incumbent firm in an enterprise software ecosystem in the transition phase. Our analysis builds on multi-perspective empirical data covering the viewpoints of all the actor types in the ecosystem, i.e., plat-form owner, platform partners, and end-user firms. The results imply the necessity of four dynamic capabilities: resource curation, ecosystem preservation, resource reconfiguration, and ecosystem diversification. With this study, we contribute to the emerging literature on the incumbent firms’ transition to a new ecosystem organising logic, and extend the study of dynamic capabilities specifically for the case of transitioning to innovation platform ecosystems.

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Persuasive system design (PSD) is an umbrella term for designs in information systems (IS) that can influence people’s attitude, behavior, or decision making for better or for worse. On the one hand, PSD can improve users’ engagement and motivation to change their attitude, behavior, or decision making in a favorable way, which can help them achieve a desired outcome and, thus, improve their wellbeing. On the other hand, PSD misuse can lead to unethical and undesirable outcomes, such as disclosing unnecessary information or agreeing to terms that do not favor users, which, in turn, can negatively impact their wellbeing. These powerful persuasive designs can involve concepts such as gamification, gamblification, and digital nudging, which all have become prominent in recent years and have been implemented successfully across different sectors, such as education, e-health, e-governance, e-finance, and digital privacy contexts. However, such persuasive influence on individuals raises ethical questions as PSD can impair users’ autonomy or persuade them towards a third party’s goals and, hence, lead to unethical decision-making processes and outcomes. In human-computer interaction, recent advances in artificial intelligence have made this topic particularly significant. These novel technologies allow one to influence the decisions that users make, to gather data, and to profile and persuade users into unethical outcomes. These unethical outcomes can lead to psychological and emotional damage to users. To understand the role that ethics play in persuasive system design, we conducted an exhaustive systematic literature analysis and 20 interviews to overview ethical considerations for persuasive system design. Furthermore, we derive potential propositions for more ethical PSD and shed light on potential research gaps.

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Agile IT projects need employees who not only follow agile structures but have a specific attitude called the agile mindset. While the relevance of the agile mindset is clear, findings on when it can be developed, are very limited. Stable personality traits, like the big five, influence attitude. Providing how these traits interact with the agile mindset gives orientation regarding in which cases an agile mindset is more trainable than in other cases. To investigate these relationships, we conducted an online survey with 327 students of a project management lecture. As a result of our SEM and QCA analysis, we found three combinations of personality traits that influence the agile mindset including different extents of conscientiousness, openness, agreeableness and neuroticism. We deepen and extend the theory around the agile mindset and enable practitioners to choose data-driven cases for development activities. Limitations and future research based on these results are given.

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Recently, businesses are introducing low-code development platforms (LCDP) that enable employees with little to no development expertise to develop their own systems to improve their work. These so-called business unit developers (BUDs) possess necessary domain knowledge to understand how to use LCDPs to create useful (self-) services. Using job resource demand theory and the job crafting model, we conceptualize that BUDs use of LCDPs can be framed using the theoretical lens of job crafting. Job crafting stems from vocational psychology and provides well-researched positive consequences, such as wellbeing and meaningfulness. Thus, our research objective is to understand how BUDs can use LCDPs to job craft to gain access to positive job crafting consequences. We interviewed 17 experts across three organizations that employ an LDCP for chatbots. Our results suggest that job crafting is a suitable framework for understanding the effects of LCDP use.

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IT support is under growing pressure to ensure efficient, flexible, and scalable use of digital technologies (Kumbakara, 2008). As a result, technical support staff is affected by monotonous work and work overload (Schmidt et al., 2022). Our research aims to augment the precarious workplace of support agents with artificial intelligence (AI). To incorporate an employee-centered perspective a priori and ensure positive impacts, we propose a framework for combining work design theory (e.g. Demerouti et al., 2001) and design science theory (Peffers et al., 2007, Niehaves & Ortbach 2016). The advances in AI promise to leverage large potential in optimizing and enhancing service processes and workplaces (Huang & Rust, 2018, de Keyser, 2019). Introducing AI into service processes, imply effects on work characteristics (Larivière et al., 2017). By combining human and artificial intelligence we propose hybrid intelligence (Dellermann et al., 2019) as a suitable solution for mitigating the persistent issues of support workers and the possible negative impacts of AI. To a great extent, IS research emphasizes the implied impacts of AI use in workplaces (Verma & Singh 2022, Wang et al., 2020). As such, work design models are widely used to empirically evaluate the impacts of AI design (Sturm & Peters, 2020), but are rarely utilized to substantiate the design of AI-augmented work systems. Only Poser et al. (2022) and Zschech et al. (2021) recently applied such models. The goal of this paper is to overcome the lack of work design in design science research (DSR) for AI-based systems and to steer the development into desired socio-technological configurations. The here presented work is expected to answer : How can work design theory inform the design of AI-augmented workplaces? RQ1 How should a hybrid intelligence system be designed to augment IT support agents’ workplaces by incorporating work design theory? RQ2 To systematically design the integration of AI, we make use of the DSR paradigm (Peffers et al., 2007). We first interview support agents and utilize the organizing move theory of Pentland (1992) and the technical support work theory of Das (2003) to ensure relevance. Based on a review of the IS literature on work design theories, we then derive a preliminary theoretical framework (Paul & Benito, 2018) RQ1. The framework represents a kernel theory for the development of meta design requirements. Contributing to the second research question, we design and subsequently evaluate the augmentation based on work-related outcomes RQ2.

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With the advent of artificial intelligence (AI), individuals are increasingly teaming up with AI-based systems to enhance their creative collaborative performance. When working with AI-based systems, several aspects of team dynamics need to be considered, which raises the question how humans’ approach and perceive their new teammates. In an experimental setting, we investigate the influence of social presence in a group ideation process with an AI-based teammate and examine its effects on the motivation to contribute. Our results show a multi-mediation model in which social presence indirectly influences whether human team members are motivated to contribute to a team with AI-based teammates, which is mediated by willingness to depend and team-oriented commitment.

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Machine learning (ML)-based conversational systems represent a value enabler for human-machine interaction. Simultaneously, the opacity, complexity, and humanness accompanied by such systems introduce their own issues, including trust misalignment. While trust is viewed as a prerequisite for effective system use, few studies have considered calibrating for appropriate trust, and empirically testing the relationship between trust and related behavior. Moreover, the desired implications of transparency-enhancing design cues are ambiguous. My research aims to explore the impact of system performance on trust, the dichotomy between trust and behavior, and how transparency might help attenuate the effects caused by low system performance in the specific context of decision-making tasks assisted by ML-based conversational systems.

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Machine learning (ML)-based software’s deployment has raised serious concerns about its pervasive and harmful consequences for users, business, and society inflicted through bias. While approaches to address bias are increasingly recognized and developed, our understanding of debiasing remains nascent. Research has yet to provide a comprehensive coverage of this vast growing field, much of which is not embedded in theoretical understanding. Conceptualizing and structuring the nature, effect, and implementation of debiasing instruments could provide necessary guidance for practitioners investing in debiasing efforts. We develop a taxonomy that classifies debiasing instrument characteristics into seven key dimensions. We evaluate and refine our taxonomy through nine experts and apply our taxonomy to three actual debiasing instruments, drawing lessons for the design and choice of appropriate instruments. Bridging the gaps between our conceptual understanding of debiasing for ML-based software and its organizational implementation, we discuss contributions and future research.

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Today formal education like higher education relies on digital learning content like learning videos or quizzes. Using such online learning material enables students to learn independently from time and place. While improvements have been made, there are still many issues as the two-year long crisis in 2020 has revealed. Many offerings do not consider the learners’ needs and can result in unsuccessful learning. One way to address these short comings is to actively include learners in the creation process of learning content. However, co-creation oftentimes relies on face to face and or group settings that may not be possible for all students at all times. Therefore, we undertake a long-term action design research project to investigate the novel concept of conversational co-creation of learning material using a conversational agent and persuasive design to engage and motivate learners. In this article we present an early-stage prototype and concept of conversational co-creation.

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To face the challenge of digital transformation as well as to implement digital innovation many incumbent companies have set up digital innovation units (DIUs). Despite a steadily growing body of knowledge, there is a rather static picture of DIUs in the literature to date, and we have little knowledge of how these units evolve over time to continuously contribute to digital transformation and innovation. To lay the foundation for an understanding of DIUs as dynamically evolving entities, we conduct a multiple-case study with DIUs of five manufacturing companies and identify DIU evolution as a process driven by an interplay of life-cycle and dialectic motor of change. In the course of this, we also outline specific triggers, sequences, and the nature of change. We generalize our findings with a conceptual process model of DIU evolution and three propositions on their current and future development to inform the existing and forthcoming literature.

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While information systems development (ISD) projects play a pivotal role in maintaining a competitive advantage, ISD project distress evolves dramatically. Given the complex and dynamic nature of ISD projects, they are prone to Escalation of Commitment (EoC), the irrational tendency to persist with failing courses of action. While EoC has been studied to a great extent in management and psychology literature, research on its role in the context of ISD project distress is fragmented, making it challenging to develop de-escalation strategies. To address this gap, we conduct a literature review on EoC in the context of ISD project distress. The proposed nomological net including triggering factors, consequences, mediators, and moderators, as well as a set of developed de-escalation strategies can serve as an inspiration and foundation for future IS researchers. By presenting this review we hope to inform future IS research to acknowledge the role of EoC in ISD projects.

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To avoid the detrimental consequences of global warming, digital nudges were recognized as effective means to steer individual behavior toward sustainability. We investigated the applications, contexts, and outcomes of green digital nudges by conducting a systematic literature review of 64 nudge interventions. We found six distinct types of nudges—priming, goal-setting, default, feedback, social reference, and framing—and 18 sustainable target behaviors (e.g., energy conservation). To explain how behavior changes through green nudges, we clustered the identified target behaviors into three behavior change outcomes: (i) altering an existing behavior, (ii) reinforcing an existing behavior, and (iii) forming a new behavior. Based on our findings, we propose guidance for researchers, practitioners, and policymakers who seek to design choice architectures that facilitate pro-environmental behavior.

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Digital transformation is often characterized as a liminal process as organizations move from established practices to new ways of organizing afforded by digital technology. Two contrasting views exist, however, on the liminality of digital transformation. One view sees liminality as a discrete transient process, while the other sees it as an on-going continuous transition. Building on a case study around a digital innovation initiative of an incumbent automotive car manufacturer, we offer a third view. We find that digital innovation triggers a phase of punctuated, multi-layered liminality that has a material, structural and temporal layer. We explain how material, temporal and structural tensions unfold at the level of practice, triggering new forms of liminal practices. We further develop three mechanisms (boundary testing, temporal bridging, and structural recoupling) that underpin punctuated multi-layered liminality. We contribute by unpacking the relationship between digital innovation and digital transformation.

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The centrality of information systems (IS) customization to match companies' needs with software systems available in the market has been researched extensively. The distinctive characteristics of Artificial Intelligence (AI) systems compared to other types of IS suggest that customization needs a new conceptualization in this context. We draw on evidence from expert interviews to conceptualize customization of AI systems as composed of four layers: data, models, algorithms, infrastructures. We identify a continuum of levels of customization, from no to complete customization. Since companies customize AI systems in response to business needs, we develop a theoretical model with six antecedents of AI systems' customization choices. In so doing, we contribute to both AI management research, by introducing the IS customization perspective in the field, and IS customization literature, by introducing AI systems as a novel class of systems and enlarging the understanding of customization for a specific class of software systems.

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Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.

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Crises become the norm for organizations, as recent years have shown. Especially the automotive industry is still facing disruptive changes such as e-mobility, connected cars or autonomous driving. Disrupted supply chains, related production downtimes and associated financial losses are consequences. Procurement departments are the interface between internal and external stakeholders in supply chains, and therefore, the central authority for managing crises. In such situations, effective decision-making is essential. Positive effects of data analytics on decision-making were part of numerous research endeavors, as well as related data analytics competencies. We conducted semi-structured interviews with experienced experts about relevant data analytics competencies in procurement departments. We present an overview specifically for procurement departments and derive implications of these competencies on decision-making. As a result, we apply our findings to existing research from a theoretical perspective and support procurement leaders and their departments in facing current and future challenges from a practical perspective.

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Although conversational agents (CA) are increasingly used for providing purchase recommendations, important design questions remain. Across two experiments we examine with a novel fluency mechanism how recommendation modality (speech vs. text) shapes recommendation evaluation (persuasiveness and risk), the intention to follow the recommendation, and how modality interacts with the style of recommendation explanation (verbal vs. numerical). Findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. They show that numerical explanations increase processing fluency and purchase intention of both recommendation modalities. The results underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal explanation style. For practice, we offer actionable implications on how to make effective sales agents out of CAs.

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The advantages offered by natural language processing (NLP) and machine learning enable students to receive automated feedback on their argumentation skills, independent of educator, time, and location. Although there is a growing amount of literature on formative argumentation feedback, empirical evidence on the effects of adaptive feedback mechanisms and novel NLP approaches to enhance argumentative writing remains scarce. To help fill this gap, the aim of the present study is to investigate whether automated feedback and social comparison nudging enable students to internalize and improve logical argumentation writing abilities in an undergraduate business course. We conducted a mixed-methods study to investigate the impact of argumentative writing on 71 students in a field experiment. Students in treatment group 1 completed their assignment while receiving automated feedback, whereas students in treatment group 2 completed the same assignment while receiving automated feedback with a social comparison nudge that indicated how other students performed on the same assignment. Students in the control group received generalized feedback based on rules of syntax. We found that participants who received automated argumentation feedback with a social comparison nudge wrote more convincing texts with higher-quality argumentation compared to the two benchmark groups (p < 0.05). The measured self-efficacy, perceived ease of use, and qualitative data provide valuable insights that help explain this effect. The results suggest that embedding automated feedback in combination with social comparison nudges enables students to increase their argumentative writing skills by triggering psychological processes. Receiving only automated feedback in the form of in-text argumentative highlighting without any further guidance appears not to significantly influence students’ writing abilities when compared to syntactic feedback.

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Product companies continue to struggle with transitioning from product-centric to servitized business models delivering product-service-software systems (PSSS). While the literature on digital servitization continues to grow, practitioners gradually infusing their business with servitized and digitized elements are still keen to understand the underlying value creation and delivery. Our study examines the complex dynamics of PSSS value architectures in light of existing value networks. Combining a longitudinal single case study spanning over three years in the construction industry with a cross-industry multiple case study approach, we collect insights from product companies, value network actors such as distributors, and customers. In an interdisciplinary approach, we integrate marketing and channel management perspectives to derive collaboration archetypes to deliver PSSSs in existing value networks and address the question of which player can contribute to the value proposition in which way. For managers, this study offers an overview of relevant change drivers shaping PSSS value architectures and their implications on essential capabilities and processes.

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Text-based conversational agents (CAs) are widely deployed across a number of daily tasks, including information retrieval. However, most existing agents follow a default design that disregards user needs and preferences, ultimately leading to a lack of usage and an unsatisfying user experience. To better understand how CAs can be designed in order to lead to effective system use, we deduced relevant design requirements from both literature and 13 user interviews. We built and tested a question-answering, text-based CA for an information retrieval task in an education scenario. Results from our experimental test with 41 students indicate that following a user-centered design has a significant positive effect on enjoyment and trust in a CA as opposed to deploying a default CA. If not designed with the user in mind, CAs are not necessarily more beneficial than traditional question-answering systems. Beyond practical implications for effective CA design, this paper points towards key challenges and potential research avenues when deploying social cues for CAs.

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Recent advantages from computational linguists can be leveraged to nudge students with adaptive self evaluation based on their argumentation skill level. To investigate how individual argumentation self evaluation will help students write more convincing texts, we designed an intelligent argumentation writing support system called ArgumentFeedback based on nudging theory and evaluated it in a series of three qualitative and quaxntitative studies with a total of 83 students. We found that students who received a self-evaluation nudge wrote more convincing texts with a better quality of formal and perceived argumentation compared to the control group. The measured self-efficacy and the technology acceptance provide promising results for embedding adaptive argumentation writing support tools in combination with digital nudging in traditional learning settings to foster self-regulated learning. Our results indicate that the design of nudging-based learning applications for self-regulated learning combined with computational methods for argumentation self-evaluation has a beneficial use to foster better writing skills of students.

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Conversational agents (CAs) provide opportunities for improving the interaction in evaluation surveys. To investigate if and how a user-centered conversational evaluation tool impacts users' response quality and their experience, we build EVA - a novel conversational course evaluation tool for educational scenarios. In a field experiment with 128 students, we compared EVA against a static web survey. Our results confirm prior findings from literature about the positive effect of conversational evaluation tools in the domain of education. Second, we then investigate the differences between a voice-based and text-based conversational human-computer interaction of EVA in the same experimental set-up. Against our prior expectation, the students of the voice-based interaction answered with higher information quality but with lower quantity of information compared to the text-based modality. Our findings indicate that using a conversational CA (voice and text-based) results in a higher response quality and user experience compared to a static web survey interface.

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Modern vehicles typically are equipped with assistance systems to support drivers in staying vigilant. To assess the driver state, such systems usually split characteristic vehicle signals into smaller segments which are subsequently fed into algorithms to identify irregularities in driver behavior. In this paper, we compare four different approaches for vehicle signal segmentation to predict driver impairment on a dataset from a drunk driving study (n=31). First, we evaluate two static approaches which segment vehicle signals based on fixed time and distance lengths. Intuitively, such approaches are straightforward to implement and provide segments with a specific frequency. Next, we analyze two dynamic approaches that segment vehicle signals based on pre-defined thresholds and well-defined maneuvers. Although more sophisticated to define, the more specific characteristics of driving situations can potentially improve a driver state prediction model. Finally, we train machine learning models for drunk driving detection on vehicle signals segmented by these four approaches. The maneuver-based approach detects impaired driving with a balanced accuracy of 68.73%, thereby outperforming time-based (67.20%), distance-based (65.66%), and threshold-based (61.53%) approaches in comparable settings. Therefore, our findings indicate that incorporating the driving context benefits the prediction of driver states.

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Anhaltende und massive Veränderungen im regulatorischen und technischen Kontext führen zu neuen bzw. veränderten Anforderungen an das Datenmanagement grosser Banken. Daraus resultieren Chancen und Herausforderungen im Datenmanagement, für die es hinsichtlich des Vorgehens und der Konsequenzen oft (noch) keine „Best Practices“ gibt. Hierzu haben wir entsprechende Erkenntnisse aus den Strategien und Umsetzungen der Partnerunternehmen der Data Management und Analytics Community (DMAC) der letzten zehn Jahre analysiert. Wir fassen den Status Quo des Datenmanagements zusammen und skizzieren wesentliche Entwicklungsrichtungen bzw. strategische Initiativen. Dabei fällt auf, dass europäische Grossbanken neben den generellen Entwicklungen in Richtung Cloud und agiler Strukturen / Prozesse auch ein zunehmend dezentrales Datenmanagement als geeigneten Weg zur datengetriebenen Organisation ansehen.

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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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The case describes the history of Bosch and its business model. Since its founding in 1886, Bosch has been a product technology company through and through. Serving primarily B2B customers in the automotive industry but growing significantly in scope and size, the German company has developed into a diversified conglomerate. Case A takes students back to the board meeting in 2009 when, in the midst of the financial crisis, the Bosch board of management was considering entering the electric bicycle market, a completely new market for Bosch with uncertain future prospects. The case asks whether Bosch should enter the electric bike market, and if so, how this opportunity should be pursued strategically and from a business model point of view.

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As a follow-up to Case A, Case B looks at further developments in the market for electric bicycles and asks what opportunities and threats lie ahead for Bosch in the light of growing competition and the bike as an IoT device.

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