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Driven by circumstances like the global pandemic many learners and educators realize the importance and value of self-regulated digital learning. To better support self-regulated learning, conversational agents have become more relevant. Conversational agents can act as tutor or as learning mate for learners. Although conversational agents have potential to better support self-regulated learning processes, challenges exist requiring implications about how to make these interactions more engaging and supportive. This study discusses the value of gamified conversational learning chatbots that use game elements to engage learners to guide researchers and practitioners to design conversational agents that effectively motivate learners and provide self-regulated learning at the same time. Therefore, we propose a systematically developed framework for gamifying educational conversational agents and contribute to theory by consolidating several theories about games, digital learning, and conversational agents and support practitioners by providing implications about what to care about when gamifying conversational agents

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Today’s servitized and digitalized society has significant implications for the economic system as value creation increasingly shifts towards digital services. Organizations are increasingly relying of artificial intelligence, which enables them to operate with novel technological artifacts like conversational agents (CA). These human-like AI-driven artifacts open new possibilities and channels for service providers to scale up their business even further and to create a business value or even to make processes more efficient. While we witness the results of that each day, we do not fully understand how such CAs can contribute to scaling up a business. Therefore, we conduct an analysis of service research and popular CAs to determine the status quo and highlight opportunities for scaling services with CAs. We contribute to theory by clarifying the meaning of scalability in combination with CAs and support practitioners by providing an overview about how organizations successfully scale their CAs.

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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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As more and more organizations reach the limits of their internal capabilities to deal with the challenges induced by digital transformation, they are increasingly forced to seek external digitalization opportunities. In particular, small and medium enterprises are affected by this due to their limited human and financial resources. Currently, there is a lack of overview of options considering limited internal digital capabilities and resources. Thus, we choose an action design research approach to develop an external digitalization activity navigator. As a result, we derive five design principles for successful navigation and 30 activities, which are presented as design pattern cards. Our work can help practitioners and scholars alike to structure external digitalization activities. After discussing the insights, we outline directions for future research. We conclude by noting the managerial implications of our findings.

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Although the nexus of Design Thinking (DT) and corporate entrepreneurship is heralded to be promising, the concrete compositional architecture of how DT manifests in practice has received limited scholarly attention. Drawing on the argument that DT can facilitate intrapreneurial innovation as it enables effectual cognition, we developed a multidimensional assessment model that measures DT for intrapreneurial innovation in an organizational context and applied it via an online survey to 547 organizations of different sizes and industries. An analysis of the dimensional and sub-dimensional values obtained from the quantitative survey data in general, and with regard to industry and firm size types in detail, enriches our understanding of DT’s manifestation in practice. We provide practitioners a useful tool to assess, benchmark, plan, analyze, and communicate the use of DT for intrapreneurial innovation, and guide future DT and entrepreneurship researchers seeking practitioner-relevant insights with nine propositions derived from our observations.

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Forthcoming
To equip students with practical capability to innovate, academic institutions are promoting industry collaboration projects where students use creative approaches, for example, design thinking, to solve complex business challenges. In creative work, the ability to reflect on the current team state and to continuously improve based on these insights is vital for effective teams. However, research on the role of digital technology to support collaborative reflection is still in its infancy. This study aims to present a digital solution that uses micro surveys and concepts of Educational Data Mining (EDM) to support collaborative reflection of students working in creativity-driven engineering projects. Using design science research, we identify design requirements of the InnoPulse tool, implement the corresponding design features and evaluate our digital solution in a real-world educational setting. Our findings pave the way for the potential of using EDM concepts to improve collaborative reflection of students using DT methods.

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Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

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get_appLino Markfort, Alexander Arzt, Philipp Kögler, Sven Jung, Heiko Gebauer, Sebastian Haugk, Christian Leyh, Felix Wortmann
Wissenschaftlicher Artikel
Purpose – The emergence of Internet of Things (IoT) platforms in product companies opens up new data-driven business opportunities. This paper looks at the emergence of these IoT platforms from a business-model perspective. Design/methodology/approach – The study applies a mixed method with two research studies: Study I–a cluster analysis based on a quantitative survey, and Study II–case studies based on qualitative interviews. Findings – The findings reveal that there is no gradual shift in a company’s business model, but in fact three distinct and sequential patterns of business model innovations: (1) platform skimming, (2) platform revenue generation and (3) platform orchestration. Research limitations/implications – The results are subject to the typical limitations of both quantitative and qualitative studies. Practical implications – The results provide guidance to managers on how to modify the components of the business model (value proposition, value creation and/or delivery and profit equation) in order to enable platforms to advance. Social implications – As IoT platforms continue to advance, product companies achieve better performance in terms of productivity and profitability, and more easily secure competitive advantages and jobs. Originality/value – The paper makes three original contributions: (1) it is the first quantitative study on IoT platforms in product companies, (2) identifies three patterns of business model innovations and (3) offers a first process perspective for understanding the sequence of these patterns as IoT platforms advance.

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The deployment of Artificial Intelligence (AI) in businesses is said to provide significant benefits to organizations. However, many businesses struggle to align single AI use cases with the overall strategic business value contribution. Thus, we investigate the strategic characteristics that determine the business value contribution of AI use cases at an organizational level. We draw on academic literature and 106 AI use cases to develop a conceptually sound and empirically grounded taxonomy of the organizational business value of AI use cases. With the developed taxonomy, decision-makers are presented with a tool to systematically align AI use cases with strategic objectives. Moreover, our findings reveal how an AI use case can generate different business value contributions in different contexts, which provides researchers with a conceptual frame for informing their empirical research endeavors at the organizational level.

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With looming uncertainties and disruptions in today’s global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and abstract to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions.

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Although Enterprise Architecture Management (EAM) is a mature discipline and widely adopted in practice, surveys indicate effectivity barriers that, at least partially, appear to be a consequence of local decision makers’ non-compliance with enterprise-wide architectural guidelines. Several recent contributions aim at extending the portfolio of EAM interventions by applying informal control mechanisms. Although promising to extend EAM effectivity, informal interventions are apparently not much utilized in EAM practice. Based on the assumption that a comprehensive presentation of design knowledge for informal EAM interventions would support more widespread adoption, this paper integrates existing knowledge components to a coherent design approach. The proposal covers theoretical justification, conceptual foundations, a taxonomy of generic informal interventions, a catalogue of derived EAM intervention types, and a process to systematically instantiate and evaluate situation-specific informal interventions. Two Action Design Research projects in large companies are summarized as evaluative evidence for the potential that comprehensive in-formal intervention design has for improving EAM effectivity.

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Marketers are often older than the audiences they are trying to reach, making it a challenge to connect. A new study examines the marketing generation gap and suggests some ways to narrow it.

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In our digital world, all companies need IT support. The IT support staff is under high pressure solving user-based issues and facing an increase of heterogeneous systems. Hybrid intelligence could solve many issues due to the combination of machine power and the individual strengths of humans. As a part of a bigger design science research project, this paper derives requirements for an IT support system based on hybrid intelligence (ISSHI). 17 requirements from the literature and 21 requirements from 24 indepth interviews with IT support managers and support-agents from three different companies have been derived. These were evaluated and refined with a second interview series of five IT support stakeholders that led to a total of 24 consolidated requirements. Finally, these requirements were used to inform a system architecture for an ISSHI. This architecture shall serve as a foundation for future research directions regarding hybrid intelligence in IT support.

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Gamification refers to the use of game-like elements in non-entertainment contexts to make activities more engaging and enjoyable to improve utilitarian outcomes. The gamification literature and the use of gamification in practice suggest that gamification can be a useful tool to support behavioral and psychological changes. Recent developments show that there is potential for new waves of gamification research. Therefore, we conducted a workshop at the International Conference on Wirtschaftsinformatik (WI) 2021 to discuss the future of gamification with interested scholars. The discussion with 25 gamification experts led to a research agenda that supports the need for adaptive and personalized gamification designs. Together with the experts, we identify three clusters for future research: 1) the personalization of gamification concepts, 2) theories and concepts for gamified human-computer interaction, and 3) the “dark side” of gamification (e.g., addiction). We also address what the gamification concept means. Aligned with the three clusters, we provide valuable starting points for future research inquiries to help researchers better understand the nature of gamification. We also discuss the capabilities and limits of gamification.

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get_appThiemo Wambsganss, Anuschka Schmitt, Thomas Mahning, Anja Ott, Sigita Soellner, Ngoc Anh Ngo, Jerome Geyer-Klingeberg, Janina Nakladal, Jan Marco Leimeister
Konferenzbeitrag
Educational process mining (EPM) offers new possibilities to discover, monitor, improve, or predict students’ learning processes using data about their learning activities captured in technology-mediated information systems (IS). Although EPM has recently attracted considerable research interest, there is still limited shared knowledge about the distinctive design characteristics of EPM from an integrative perspective. To address this gap, we conducted a systematic literature review to identify EPM characteristics. Building on a technology-mediated learning perspective, we develop a taxonomy that classifies EPM characteristics into four major categories (i.e., purpose, user, input, analysis). We evaluate and refine our taxonomy with ten domain experts, identified three clusters in the reviewed literature, and derived six archetypes of EPM scenarios based on our categorization. Finally, we formulate a novel research agenda to guide researchers in systematizing and synthesizing research on different technological embeddings of EPM in a students’ learning process.

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Behavioral experiments are a highly suitable method for testing theories, as they can establish causality while controlling for other confounding factors. However, researchers that aim to conduct and publish such studies face various concerns about the methodological approach. A lack of clarity exists in our field as to which related practices and design decisions are legitimate and accepted. To address this issue, we present a structured literature review that analyzes the designs of 168 behavioral experiments published in the Senior Scholars’ Basket of journals. We find that most experiments are confirmatory, individual-level, between-subjects laboratory experiments. At the same time, we find that some under-represented experiment designs, such as exploratory online experiments, may bear potential for identifying new behaviors and constructing new or proper-to-IS theories. This paper contains an in-depth discussion on the findings and provides decision support to IS researchers that seek to design and publish behavioral experiments.

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The role of the Chief Information Officer (CIO) in the organization has received a lot of attention in recent years. While traditionally most CIOs had faced the difficulty of stepping out of the shadow of being coined as a "Utility & Infrastructure Director", they have been found to establish themselves as a driving force in defining and shaping the digital agenda and strategic direction of their organization (Peppard et al. 2011). This crisis-driven development is, however, now paving the way for a new era of the CIO role. As research shows, crises usually do not lead to a trend reversal, but to a trend acceleration (Gassmann and Ferrandina 2021). Therefore, this opportunity should be seized by CIOs in order to leverage the digitalization momentum gained through the COVID-19 crisis, and to build lean digital organizational structures and use strategic sourcing of services for cost efficiency. Thus, the focus here should not be on rebuilding old barriers, but to use the crisis induced dynamic to empower the CIO to successfully master the future challenges of efficiency, flexibility, resilience, scalability and innovation in the organization.

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Since the emergence of conversational agents, this technology has seen continuous development and research. Today, advanced conversational agents are virtually omnipresent in our everyday lives. Albeit the numerous improvements in their conversational capabilities, breakdowns are still a persistent issue. Such breakdowns can result in a very unpleasant experience for users and impair the future success of conversational agents. This issue has been acknowledged by many researchers recently. However, the research on strategies to overcome conversational breakdowns is still inconclusive, and further research is needed. Therefore, we conduct a systematic literature analysis to derive conceptual conversational breakdown recovery strategies from literature and highlight future research avenues to address potential gaps. Thus, we contribute to theory of human-agent interaction by deriving and assessing recovery strategies and suggesting leads for novel recovery strategies.

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Voice assistants are a novel class of information systems that fundamentally change human–computer interaction. Although these assistants are widespread, the utilization of these information systems is oftentimes only considered on a surface level by individuals. In addition, prior research has focused predominantly on initial use instead of looking deeper into post-adoption and habit formation. In consequence, this paper reviews how the notion of habit has been conceptualized in relation to biographical utilization of voice assistants and presents findings based on a qualitative study approach. From a perspective of post-adoption users, the study suggests that existing habits persist, and new habits hardly ever form in the context of voice assistant utilization. This paper outlines four key factors that help explain voice assistant utilization behavior and furthermore provides practical implications that help to ensure continued voice assistant use in the future.

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The internet of things (IoT) and digital platforms have offered industrial companies new opportunities to compete against digital platform-native companies. To succeed in this competition, industry incumbents must learn to extend their traditional product and service business through platforms. However, extant research has focused primarily on the ability of platform owners to govern mature platforms for innovation but has largely ignored how industry incumbents can build these capabilities internally during the establishment of their platform ecosystems as part of larger transformational journeys. To address this gap, we conduct a multiple case study of three incumbent organizations, drawing on a dynamic capability lens. We identify 11 practical microfoundations of sensing, seizing, and reconfiguring dynamic capabilities that aided three incumbents in establishing their IoT platform ecosystems. Besides the transformational activities, our findings contribute to the literature on platform establishment through three IoT-related shifts that deviate from known digital platform paradigms.

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Design science research (DSR) aims to generate knowledge about innovative solutions to real-world problems. A comparably new stream of research, DSR has matured methodically, and is increasingly catching the interest of researchers, specifically for its potential to contribute to problem solving in society and the economy. Since research methodology curricula develop slowly, however, DSR is still underrepresented in most curricula and courses on research design and methods, and we lack guidance on what and how to teach in a DSR course in a way that enables junior academics to conduct DSR according to high standards. We report on teaching DSR methodology both on PhD and Master levels and for both managerially and technically oriented student populations. Our interactive on-site and distance formats have been refined over 14 years. The PDW presents an effective syllabus, teaching material and experience from conducting over 25 courses with students from over 20 countries across all three geographic AIS regions.

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Driven by economic advantages and the idea of disintermediation of business processes, the decentralization of technical and economic systems has become a highly discussed topic in recent years. Extant research primarily investigated the technical implementations of decentralized information systems (IS) and their use by firms in business networks. It became clear that interorganizational relationships and business functions must be transformed to enable the use of decentralized IS (e.g., those related to how firms can be involved in the design, instantiation, operation, and governance of decentralized IS). However, the impact of transforming business functions of individual firms remains largely unclear, obfuscating a comprehensive understanding of the implication of decentralized IS use on internal organizational structures of firms. In this work, we focus on the identification of challenges for firms in using distributed ledger technology (DLT as a representative for decentralized IS) and their effects on the business functions of firms.

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Facilitated by Artificial Intelligence technology, cognitive automation means to front and back offices what the pervasive automation through physical machinery and robots meant to production plants. Thus, we can automate tasks and processes that were unimaginable to be automated one decade ago. However, organizational adoption of cognitive automation is way below its possibilities, as this novel class of automation technology is perceived to be risky by organizations. This demands structured approaches for assessing the suitability of use cases for cognitive automation. Following the Design Science Research paradigm, we develop a method for assessing cognitive automation use cases. This enables practitioners to make more informed decisions on selecting, specifying, and embedding cognitive automation use cases in their organizations. For researchers, the method serves as a conceptual frame, which they can adapt to guide their empirical research or to use it for developing future decision support to shape the future of work.

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This study contributes to the growing body of research on the assimilation and institutionalization of enterprise architecture (EA) within organizations. It adopts an institutional change lens to longitudinally analyze EA’s institutionalization, de-, and re-institutionalization processes in one of Norway's large public sector organizations. The study demonstrates a dynamic and cycle model of EA institutionalization in response to both internal and environmental pressures. It specifically emphasizes on regaining legitimacy for EA after getting de-institutionalized by revisiting its classical premise and by adapting to contemporary organizations’ agile mode of organizing.

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Owing to the necessity of effectively establishing enterprise architecture (EA) in an organizational context, there is a growing stream of research to examine the assimilation and institutionalization of EA in organizations. Our study aims to contribute to this stream by giving rise to the legitimacy of EA as the cornerstone of its institutionalization. To this end, we investigate four criteria of legitimacy, namely regulatory, pragmatic, normative, and cultural-cognitive legitimacy, in a case organization that lost legitimacy for its EA practices. We found criticality of regulatory and pragmatic legitimacy that need to be obtained effectively and promptly in order to grant sufficient time for normative and cultural-cognitive types of legitimacy to be attained.

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By acquiring complementors, digital platform owners can facilitate rapid advances in the evolution of their platform ecosystems. We describe how Salesforce has successfully evolved its platform ecosystem through the acquisition of complementors. Based on insights from the Salesforce case, we provide recommendations for acquiring complementors, aligning acquisitions with the platform owner’s proprietary developments, integrating the acquired complementors and retaining the coherency of the platform’s offerings even after diverse acquisitions.

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get_appHartmut Schulze, Oliver Bendel, Maria Schubert, Mathias Binswanger, Monika Simmler, Ricarda T.D. Reimer, Alexandra Tinner, Andreas Urech, Jeanne Kreis, Nicole Zigan, Iris Kramer, Silvan Flückiger, Cäsar Künzi, Kathrin Kochs, Olivia Zingg
Monograph
The spectrum of changes that enterprises need to deal with varies from simple continuous adjustments of the product portfolio in response to evolving customer preferences, to complete overhauls of the business and operating model in response to disruptive trends. Many research fields and practitioner disciplines have produced analysis and engineering approaches that can help enterprises to assess and prepare for the impact of changes from this spectrum. However, they have partial scopes and consequently limited integration. By selecting, slightly extending and integrating existing approaches, this paper introduces a ‘simple enough’ integrated solution model and a ‘simple enough’ integrated analysis and engineering method that covers the full spectrum of changes. Our focus is the large, complex enterprise that operates in a specific industry and performs information processing at scale. The research is intended to provide methodical support to practitioners with a responsibility for shaping solutions. Our proposal is the result of initial experiences in practice that instilled the research theme, application in a large-scale industry project, focused collaborative research that joined researchers and academia, and ongoing applications and experiences in practice. The solution model and the analysis and engineering method that we propose support three types of adaptability: a) foundational adaptability produces full new business model and operating model parts, b) transitional adaptability extends the current business model and operating model and adds additional configurability, and c) routine adaptability is managed within the configurability of individual operating model components that need to be designed with sufficient bandwidth. A business configuration center is proposed as a key constituent that manages the differences in underlying technology, and that allows to perform integrated, technology agnostic administration of an industry solution.

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get_appSimon Föll, Martin Maritsch, Federica Spinola, Varun Mishra, Filipe Barata, Tobias Kowatsch, Elgar Fleisch, Felix Wortmann
Wissenschaftlicher Artikel
Background and Objective: Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction. Methods: FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions – a basis for a wide variety of ML algorithms. Results: We evaluated FLIRT on the publicly available WESAD dataset, which focuses on stress detection with an Empatica E4 wearable. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered. In the classification task, FLIRT outperforms the preprocessing baseline of the original WESAD paper. Conclusion: FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs.

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get_appClaude Draude, Christian Gruhl, Gerrit Hornung, Jonathan Kropf, Jörn Lamla, Jan Marco Leimeister, Bernhard Sick, Gerd Stumme
Wissenschaftlicher Artikel
Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Strukturbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Systeme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Abschließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.

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Businesses of all kinds need to innovate rapidly and adapt quickly due to market disruption fueled by the digital transformation. Executive managers require the management tool that supports them in navigating their company through agility transformation journey by identifying and developing organizational capabilities as means to enhance Corporate Agility.

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Even though organizations may plan for long-term enterprise-wide ob-jectives, they are shaped by local decision-maker’s actions. The latter tend to have conflicting goals, such as short-term and immediate satis-faction of local business needs over organization-wide objectives. While local and diverse decision-making enables specialized products and services, ungoverned behavior may lead organizations that are hard to control and manage. Hence, the challenge is to harness, rather than eliminate decentral autonomy by reaping its benefits while limit-ing its downsides. Pursuing this purpose, this Design Science Research (DSR) study presents the creation and evaluation of a governance mechanism: a nudge-based label. It also contains a set of design fea-tures, which are evaluated quantitatively and qualitatively with expert surveys and discussions. The contributions include design knowledge about labels and the investigation of nudging as an intra-organizational governance mechanism.

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Digitales Lernen ist ein zentraler Bestandteil der heutigen Bildungslandschaft geworden. Insbesondere Lernvideos haben in den letzten Jahren deutlich an Popularität gewonnen und Plattformen wie YouTube sind heute ein fester Bestandteil von Lernprozessen. Bildungseinrichtungen haben diese Entwicklung erkannt und Lernvideos in ihre Lernsysteme integriert. Allerdings sind die heutigen Lernvideos oft nicht sehr ansprechend, weil es an Gestaltungswissen mangelt, wie Interaktionen und gamifizierte Elemente die Motivation und den Lernerfolg steigern können. Dieser Mangel an Gestaltungswissen führt zu minderwertigen Lernvideos, die den Lernprozess der Lernenden meist nicht in dem Maße unterstützen, wie sie es könnten. Um effektive Lernvideos zu entwickeln, welche die Lernenden adäquat unterstützen, ist handfestes und praktisches Gestaltungswissen für die Lehrenden notwendig. Vor diesem Hintergrund entwickeln wir Entwurfsmuster für interaktive gamifizierte Lernvideos, um Praktiker*innen bei der Erstellung effektiver Lernvideos anzuleiten, die die Motivation und das Engagement von Lernenden zu fördern, welche sie aktiv in ihrem Lernprozess unterstützen zu können. Wir setzen damit zusätzlich die Grundlage für zukünftige Forschung zur Gestaltung effektiver Lernvideos. Dabei folgen wir einem gestaltungsorientierten Forschungsansatz, um die Entwicklung unserer Entwurfsmuster zu systematisieren.

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Eine der kritischsten Aufgaben für Start-ups ist die Validierung ihres Geschäftsmodells. Daher versuchen Gründer, Informationen wie z. B. Feedback von anderen Akteuren zu sammeln, um die Gültigkeit ihrer Annahmen zu bewerten und Entscheidungen zu treffen. Bisherige Arbeiten über Entscheidungshilfen für die Validierung von Geschäftsmodellen bieten jedoch keine Lösung für den höchst unsicheren und komplexen Kontext von Start-ups in der Frühphase. Der Zweck dieser Studie ist daher die Entwicklung von Entwurfsprinzipien für ein hybrides Entscheidungsunterstützungssystem (HIDSS), das die komplementären Fähigkeiten von menschlicher und maschineller Intelligenz kombiniert. Wir verfolgen einen gestaltungsorientierten Forschungsansatz, um ein Prototyp-Artefakt und eine Reihe von Designprinzipien zu entwerfen. Unsere Studie liefert präskriptives Wissen für HIDSS und trägt zu früheren Arbeiten über Entscheidungsunterstützung für Geschäftsmodelle, über die Anwendung der komplementären Stärken von Mensch und Maschine zur Entscheidungsfindung und über Unterstützungssysteme für extrem unsichere Entscheidungsprobleme bei.

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The algorithms of artificial intelligence are constantly being further developed and are being used in more and more products and applications in business and society. Numerous prototypes are being developed to open up the use of artificial intelligence in a wide variety of application areas. Nevertheless, only a few prototypes succeed in making the leap into productive applications that create sustainable business benefits. This paper series shows that processes and structures are needed for the management of artificial intelligence to ensure the sustainable success of AI systems.

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An unhealthy lifestyle is one of the key factors of mortality in modern society. Persuasive technologies, information systems that aim to change users behaviors and attitudes in a predefined way, are increasingly used for health promotion to encourage a physically active lifestyle and healthful nutrition to prevent diseases of affluence. Persuasive technologies for health come in different shapes, though, recent advances in mobile and sensor technologies have given rise to wearable self-tracking devices that have shortly reached high penetration in mainstream markets. While high hopes lie on persuasive technologies to improve personal and public health outcomes and empirical evidence for their effectiveness exists, concerns regarding their capacity to sustainably induce changes in health behaviors are growing. Despite the rich body of literature on the adoption of persuasive technology, gaps in knowledge exist with regard to users post-adoptive use experiences and behaviors, the processes along which persuasive technologies generate cognitive-affective and behavioral outcomes, and how persuasive technologies can impel such processes in a targeted way. This cumulative dissertation addresses these gaps and seeks to gain an understanding of post-adoptive use processes, to explain the occurrence of adverse experiences and their effects on users, to explain the outcomes of persuasive technology use, and to generate design knowledge to develop persuasive technologies to achieve long-term changes in health-promoting behaviors. These research objectives are achieved through eight research papers using different research approaches to shed light at different aspects concerning the post-adoptive use, the behavioral outcomes, and the design of persuasive technologies, by applying qualitative and quantitative research methods, and following the design science research paradigm. The dissertation makes several primary contributions to persuasive technology research: First, users habit formation processes and adverse experiences are highlighted. Second, the outcomes of persuasive technology use are outlined and mechanism-level explanations for why some users arrive at behavioral outcomes while others do not are provided. Third, by presenting a design artifact, design knowledge for the development of adaptive persuasive technologies for health promotion is generated. Moreover, the findings have important implications for practitioners, in particular, providers of persuasive technology and system designers.

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Currently, more and more learning environments exist in whole or in part online. Although these learning environments can be scaled to large audiences, the lack of interaction between educators and students is difficult to replace and remains a primary challenge in the field. This is why Smart Personal Assistant Technology (SPAT) has received attention in the field. SPAT describes computer systems intended to converse with humans. These systems may alleviate the problem of individual support in online and blended learning environments by engaging in natural interaction with the students. Although SPAT seems to be promising for education, three important challenges related to SPAT design emerge and will be tackled in this dissertation. Research Challenge (RC) 1: There is limited knowledge about existing SPAT and corresponding requirements of SPAT in online and blended learning environments. RC2: There is limited knowledge of how to design SPAT to improve students learning outcomes. RC3: It is not yet clear how non-tech-savvy educators can design and use SPAT on their own to offer individual support. This dissertation tackles these RCs by following a multi-method approach that identifies research gaps and requirements of SPAT in online and blended learn-ing environments in order to propose design principles (DPs) and design knowledge regarding how educators can use the technology. The dissertation has three main core findings. First, the findings show that there is a lack of consensus on how to design and develop SPAT for online and blended learning environments. Second, based on the first finding, this study proposes five DPs on how to design SPAT in these contexts and investigate the effectiveness of these principles in a series of laboratory and field experiments across different school levels (secondary education, higher education) and school types (high school, vocational business school, university). The findings show that these DPs can have a positive influence on students learning processes resulting in increased students learning outcomes. It also addresses the third RC by developing a SPAT Platform that empowers non-tech-savvy educators to create their own SPAT in order to make this technology accessible to educators who are not technically inclined. The dissertation contributes to a theory of design and action that explains how non-tech-savvy educators can design effective SPAT in their online and blended learning environments.

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Die Verbindung von menschlicher und künstlicher Intelligenz, die wir hybride Intelligenz (HI) nennen, soll Mitarbeitende im IT-Support entlasten und unterstützen. In diesem Beitrag gestalten wir nutzungszentriert eine HI-basierte Dienstleistung am Beispiel des IT-Supports. Wir beginnen mit einer Problemidentifizierung am Beispiel von drei unterschiedlich groß aufgestellten Unternehmen. Basierend auf diesen identifizierten Herausforderungen des IT-Supports wurden die Interaktionen während der Dienstleistungserbringung zwischen allen Beteiligten (Mitarbeitende, Kund:innen und System) nutzungszentriert neu gestaltet.

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While digital transformation as a research phenomenon per se gains acceptance in both practice and academia, its precise conceptualization and theoretical underpinnings are discussed. To address these gaps, we propose a critical realist view on Digital Transformation. Building on a literature review on generative mechanisms in digital transformation studies, we show how the concept of generative mechanisms is used and what types of generative mechanisms are adopted. Based on our findings, we propose a research agenda that encourages further studies on digital transformation, taking a critical realist stance. Thus, we put forward ways of applying critical realism to digital transformation studies and show how critical realism can advance research through its ability for multi-level analysis and its unique understanding of change and transformation.

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Digital nudging is a promising approach from behavioral economics. In deci-sions where individuals tend to struggle, nudges can support users of digital systems by aligning their behavior with their preferences. Despite their wide use, most digital nudges are designed to support the intended behavior from the perspective of a company while neglecting potential legal, ethical, or individual constraints or preferences. With modern technologies such as artificial intelli-gence or big data, these issues multiply and with the increasing effectiveness of digital nudges and use of new technologies, this has become even more critical. Thus, in this paper we follow a Design Science Research approach to develop a process model for the systematic development of legitimate nudges (DELEN). Legitimacy requires that dealings between different entities shall be fair. Unlike other models, we set normative boundaries derived from literature, expert in-terviews, and target group segmentation as integral elements. Target group seg-mentation increases nudge effectiveness and avoids unnecessary burdens for other individuals. By doing so, the DELEN process model paves the way for legitimate and effective digital nudges.

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Volatility, uncertainty, ambiguity, and complexity (VUCA) are drivers in today’s business world. Therefore, an increasing number of organizations implement agility (agile methods) to cope with the rapid and accelerating change. However, these agile transformations do not succeed unhindered, and some fail. One reason for that is the employees lacking an agile mindset and they do not support or participate in the transformation. So far, knowledge of the agile mindset is in its infancy. A conceptualization for developing a measuring instrument on the individual level is lacking. Furthermore, the relevance of the agile mindset regarding organizational performance in a VUCA world is still unclear. Our study aims to close this gap. To do so, we examine the agile mindset with 15 qualitative interviews and conduct an online survey (N=449) to predict organizational agility and organizational performance based on the agile mindset. As for results, we conceptualize the agile mindset as an attitude that comprises four dimensions: ’learning spirit’, ‘collaborative exchange’, ‘empowered self-guiding’, and ‘iterative value creation’. We have found an indirect effect of the agile mindset affecting organizational performance towards organizational agility. These findings contribute to the agility and management research by providing both a conceptualization and a measuring instrument for the agile mindset. Furthermore, the agile mindset´s relevance in agile transformation is explained and its relation to organizational performance outlined. For successful agile transformation, executives must focus on the agile mindset of their employees and foster this by e.g. being a role model.

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We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.79 for the components and the π = 0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.

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Business ecosystems are dynamic network structures of autonomous actors who contribute individual resources and capabilities towards a shared purpose. This study proposes a design theory to develop a model that describes the fundamental value creation activities in business ecosystems. This research endeavor is embedded in a consortium research project following design science and builds on both the current body of academic knowledge and the experience of. The paper makes three main contributions. Firstly, the study defines a specific problem space, identifies justificatory knowledge, and develops design requirements and principles as the theory’s core. Secondly, a reference model is developed that describes value creation, providing a conceptual grid for further theoretical analysis. Thirdly, the model provides an analytical framework for practitioners, who need to understand which activities should be performed and how these activates should be configured to generate advantages relative to others.

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Im Rahmen einer Lehrveranstaltung des Masters in Business Innovation im Frühjahr 2021 gelang es, ohne vertiefte Kenntnisse der Bäckereibranche durch Verwendung bereits vorhandener Daten und durch Nutzung von Methoden der Statistik und der Künstlichen Intelligenz die Verkaufsprognosen der Filialleiterinnen und Filialleiter zu übertreffen und die Verkäufe des folgenden Tags mit einer Abweichung von ca. 13% bis 14% vorherzusagen. Diese unerwartet guten Ergebnisse lassen den Schluss zu, dass der Einsatz von Methoden der Statistik und der Künstlicher Intelligenz auch in KMUs wie Bäckereien in Zukunft wettbewerbsrelevant sein wird. Wenn es gelänge, die Prognosequalität zu verbessern, wäre in Bäckereien Nutzen in zwei Richtungen zu erwarten: Der unternehmerische Erfolg kann gesteigert und der Foodwaste reduziert werden. Allerdings bräuchte es mehr und bessere Daten, weiteren Austausch mit Experten der Domäne sowie geeignete Apps, um datengetriebene Prognosen in den Bäckereien zu implementieren.

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Argumentation is an omnipresent foundation of our daily communication and thinking. The ability to form convincing arguments is not only the fundament for persuading an audience of novel ideas but also plays a major role in strategic decision-making, negotiation, and productive civil discourse. However, students often struggle to develop argumentation skills due to a lack of individual and instant feedback in their learning journey, since providing feedback on the individual argumentation skills of learners is very time consuming and not scalable if conducted manually by educators. Following a design science research approach, we propose a new class of argumentation learning systems that provide students with individual and ongoing tutoring to support them in learning how to argue. We build our socio-technical design on a combination of user-centered design principles, a conceptualization of argumentation structures in student-written text, and Natural Language Processing and Machine Learning classifiers to provide individual feedback. To investigate if the new system class of AI-based argumentation tutoring systems helps students to improve their argumentation skills, we evaluated the novel artifact class in two empirical studies in comparison to traditional argumentation learning systems. In a laboratory experiment (study 1), as well as in a field experiment in a large-scale lecture over three months (study 2), we found that AI-based argumentation tutoring systems based on our design principles, argumentation schemes, and algorithms improve the short- and long-term argumentation skills of students significantly compared to the traditional argumentation learning approaches.

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