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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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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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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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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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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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