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Innovation is one of the most important antecedents of a company's competitive advantage and long-term survival. Prior research has alluded to teamwork being a primary driver of a firm's innovation capacity. Still, many firms struggle with providing an environment that supports innovation teams in working efficiently together. Thereby, a team's failure can be attributed to several factors, such as inefficient working methods or a lack of internal communication that leads to so-called innovation blockages. There are a number of approaches that are targeted at supporting teams to overcome innovation blockages, but they mainly focus on the collaboration process and rarely consider the needs and potentials of individual team members. In this paper, we argue that Conversational Agents (CAs) can efficiently support teams in overcoming innovation blockages by enhancing collaborative work practices and, specifically, by facilitating the contribution of each individual team member. To that end, we design a CA as a team facilitator that provides nudges to reduce innovation blocking actions according to requirements we systematically derived from scientific literature and practice. Based on a rigorous evaluation, we demonstrate the potential of CAs to reduce the frequency of innovation blockages. The research implications for the development and deployment of CAs as team facilitators are explored.

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Voice assistants’ (VAs) increasingly nuanced and natural communication opens up new opportunities for the experience of users, providing task assistance and automation possibilities, and also offer an easy interface to digital services and ecosystems. However, VAs face various problems, such as low adoption and satisfaction rates as well as other negative reactions from users. Companies, therefore, need to consider how individuals utilize VAs and what contributes to user satisfaction. Key for the design of VAs are their unique affordances and their agentic nature that distinguish these IT artifacts from non-agentic IS. A configurative and dynamic approach enables to shed light on the complex causalities underlying user outcomes with these novel systems. Consequently, we examine in this study how individuals actualize the affordances of VAs during the initial adoption stage. For this purpose, we draw on a diary study research design that examines affordance actualization processes with new VA users. We examine with a configurational approach, how the actualization of VA affordances contributes to the outcomes of VAs, i.e., in our case user satisfaction. The results of our diary study show distinct patterns of functional affordance configurations. In addition, we show that affordances unfold and evolve over time. The derived implications provide a configurative theoretical understanding for the role of VAs affordances for user satisfaction that provides practitioners useful guidance to actualize the potential of VAs.

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Agile work organization has experienced a significant increase in acceptance in recent years. However, literature falls short in explaining the transformation process that results from the implementation of agile work organization as a means of achieving increased adaptability, rate of speed and flexibility. We apply a process ontology to agile work organization by following three multinational firms that apply agile work organization, utilizing a case study approach over three years. At the macro level, our theory describes the transformation process set in motion by agile work organization as a three-phase process. At the micro level, we show that this transformation process is driven by specific design decisions on individual elements. Thus, our process theory contributes to a better understanding of agile work organization as a means to achieve organizational agility and to STS theory by showing that the emergence and constitution of STS are mainly driven by micro-level processes.

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Artificial Intelligence (AI) shows great potential to tackle environmental sustainability issues that are critical to the survival of Humanity and Planet Earth. However, the development and use of AI causes indirect emissions leading to detrimental effects on the environment. Therefore, it is important for organizations, researchers, and practitioners in the Information Systems (IS) domain to understand both the positive and negative effects of AI on the environment. This article contributes to this topic by performing a theoretical review of the literature at the intersection of AI and Sustainability to determine the current research streams. Further, this article adopts the affordance theory as a theoretical lens with the goal to identify the affordances of Sustainable AI – a field that encompasses the research areas ‘AI for Sustainability as well as ‘Sustainability of AI’ – in the Green IS community. The identified affordances would enable researchers and practitioners to design and use Sustainable AI systems.

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Wissenschaftlicher Artikel
Technical debt (TD) is a technical compromise wherein the ability to maintain information technology (IT) applications over the long term is sacrificed for short-term goals. TD occurs when software development teams undergo constant pressure to release applications swiftly, on a tight schedule. The accumulation of TD, which often leads to a significant cost surplus, presents a ubiquitous challenge in technology-driven organisations. To keep TD levels under control, many organisations implement top-down mechanisms that impose enterprise-wide principles on software development teams. This clinical research presents a complementary but distinct approach to managing TD. A digital nudge was introduced at Credit Suisse, a global financial services company, to help raise awareness and understanding, and stimulate actions related to TD decision-making in software development teams. This paper reports on the nudge’s clinical design, implementation, impact, and evaluation. As the nudge was effective in reducing TD in IT applications after one year of use, we demonstrate that digital nudges are viable means for guiding collective decisions in complex decision environments like that of TD management. Our findings have several implications for research and practice.

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Wissenschaftlicher Artikel
IT carve-out projects are complex and cost-intensive components of M&A transactions. Existing research sheds little light on the determinants of IT carve-out project complexity and/or its effects on divestor performance. Instead, research has focused on the post-acquisition IT integration project and acquirer performance. This paper presents the first divestor-centric model of IT transactions from the divestor to the acquirer when a Business Unit in a Multi-Business Organization (MBO) is carved out and integrated into another MBO. The model explains how divestor business and IT alignment pre-conditions contribute to increased IT carve-out project complexity. Such complexity increases IT carve-out project time to physical IT separation and creates IT stranded assets, which decrease post-divestment business, IT alignment and divestor performance. The current recommended strategy of adopting transitional service agreements (TSAs) to handle IT carve-out complexity is compared with two new proactive strategies derived from the model. TSA-based strategies restrict the divestor from both decommissioning IT stranded assets and reconfiguring its IT assets to support its new post-divestment business strategy. The two new strategies address IT carve-out complexity without incurring the negative effects from adopting TSAs

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Designing for system trustworthiness promises to address challenges of opaqueness and uncertainty introduced through Machine Learning (ML)-based systems by allowing users to understand and interpret systems’ underlying working mechanisms. However, empirical exploration of trustworthiness measures and their effectiveness is scarce and inconclusive. We investigated how varying model confidence (70% versus 90%) and making confidence levels transparent to the user (explanatory statement versus no explanatory statement) may influence perceptions of trust and performance in an information retrieval task assisted by a conversational system. In a field experiment with 104 users, our findings indicate that neither model confidence nor transparency seem to impact trust in the conversational system. However, users’ task performance is positively influenced by both transparency and trust in the system. While this study considers the complex interplay of system trustworthiness, trust, and subsequent behavioral outcomes, our results call into question the relation between system trustworthiness and user trust.

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get_appNaim Zierau, Christian Alexander Hildebrand, Anouk Samantha Bergner, Francesc Busquet I Segui, Anuschka Schmitt, Jan Marco Leimeister
Wissenschaftlicher Artikel
Voice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. The current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). Moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). The findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. These findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers' service experiences and the theory-driven ''conversational design'' of voice-based interfaces.

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Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.

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