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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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