Artificial Intelligence-based Assistants and Platforms

Permanent URI for this collectionhttps://hdl.handle.net/10125/107505

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Adopting Generative AI for Literature Reviews: An Epistemological Perspective
(2024-01-03) Schryen, Guido; Marrone, Mauricio; Yang, Jiaqi
Artificial Intelligence (AI) and machine learning are becoming increasingly influential in research. Within this context, generative AI tools like ChatGPT serve two main purposes: the improvement of writing (communication goal) and the generation of new ideas (innovation goal). The latter goal has been less explored and is therefore the focus of this article. We specifically look at how these generative AI tools can aid in the development of literature reviews within the realm of information systems research. We adopt an epistemological lens, distinguishing various knowledge-building activities. Our analysis evaluates how well generative AI tools support these tasks and offers insights tailored to different types of literature reviews.
Use or Not: A Qualitative Study on the User Adoption and Abandonment of Voice Shopping with Smart Speakers
(2024-01-03) Lan, Jiahe; Chen, Yu; Yan, Zheng
The recent advances in smart speakers impel the emergence and prevalence of voice shopping – placing orders on voice assistants. Previous work has studied user acceptance of voice shopping and the factors influencing users’ experience of voice shopping. However, despite the growing interest in the use of voice shopping, little is known about the limited usage or abandonment of voice shopping. In this paper, we address this research gap through a qualitative study of 43 users of Tmall Genie, a smart speaker popular in China. We found that participants are willing to make low-involvement purchases via voice shopping. However, after a period of use, participants tend to limit or abandon voice shopping due to time-consuming interaction and mistrust of voice shopping. Based on our findings, we discuss how our study could advance the understanding of voice shopping and present implications for researchers and practitioners on technical robustness, adaptive conversational product presentation, and cross-platform product recommendations for the future design of voice shopping systems.
Higher-Order Externalities in Multi-Platform Ecosystems
(2024-01-03) Schmidt, Rainer; Alt, Rainer; Zimmermann, Alfed
Platforms have become pivotal business models and involve a different logic than traditional pipeline business models. Important factors for understanding their emergence and growth are externalities such as network effects and complementarities. At present, these concepts are focused on the effects on a single platform, but with the diffusion of platforms and their maturity, platforms are increasingly linked to each other. This interconnection of multiple platforms towards multi-platform ecosystems poses two key challenges. First, their networked structure exceeds traditional analytical approaches that are based on dyadic relationships. Second, individual choices drive externalities in these ecosystems, giving rise to emergent structures. To address these issues, the present research proposes a network science-based methodology that augments existing approaches to understand and visualize ecosystems (“ecosystem intelligence”). It presents a network conceptualization that captures the structure of multi-platform ecosystems and proposes a method for data collection and detailed network modeling. Among the main findings are three new types of externalities referred to as higher-order externalities. These include remote externalities that indicate value creation across platforms, transitive externalities representing chains between platforms, and polyadic externalities capturing value creation in n-ary relationships. They contribute to the understanding and management of the intricacies of multi-platform ecosystems, which can open new avenues in ecosystem intelligence.
Creative Assistants with Style: Making Sense of Generative AI as “Style Engines”
(2024-01-03) Peter, Sandra; Riemer, Kai
Generative AI technologies have been heralded for their ability to become powerful assistants, creating plausible text and realistic images. Yet they have also frequently been criticized for their lack of precision, accuracy or veracity. We argue that focusing on such traits misses what is most novel and defining about generative AI. As probabilistic technologies, generative AIs do not store, in any traditional sense, any data or content. Rather, essential features of training data become encoded in deep neural networks as patterns, or what we refer to, as styles. We discuss what happens when the distinction between objects, their properties, and appearance dissolves and all aspects of images and text become understood as styles, accessible for exploration and creative combination. We suggest that the ability to explore the world with styles is a defining feature of generative AI, with significant implications for how we assess its usefulness as creative assistants.
When and How to Implement Choices on Customer Service Chatbots
(2024-01-03) Han, Elizabeth; Yin, Dezhi; Zhang, Han
Many service chatbots are equipped to provide choices when interacting with customers to streamline the service delivery process. This research investigates when and why the implementation of choices enhances or impairs customers’ service experience. Based on the concept of fluency, we posit that the choice implementation is beneficial only after a conversational breakdown due to a chatbot failure; otherwise, the value of choice provision for facilitating fluency may not be salient enough. We further propose that choice provision is counterproductive when the choice set is incomprehensive, reducing (rather than enhancing) the fluency in the use of provided choices for a subsequent decision. We conducted several experiments to test these hypotheses. By illuminating when and why choice implementation may help or harm customers during a chatbot-initiated service interaction, we augment the current understanding of a chatbot’s role in customers’ service experience and provide insights for the deployment of choice-equipped service chatbots.
User Preferences for Interaction Modalities: The Influence of Task, Context, and User Characteristics when Interacting with Conversational Agents
(2024-01-03) Riefle, Lara; Benz, Carina
When using conversational agents (CAs), the interaction is typically either text- or speech-based. Existing research focuses on the effects of these interaction modalities or the general adoption of either text- or speech-based interaction, leaving an important research gap regarding users’ underlying preferences for interaction modalities. Therefore, this study investigates the influence of task, context, and individual user characteristics on user preferences for interaction modalities. We use a two-step approach consisting of exploratory interviews to identify 14 influencing factors, followed by a scenario-based experiment to quantitatively assess the impact of the identified task, context, and user characteristics. The results provide insights into the drivers for users’ preferences for interaction modalities when interacting with CAs. Thereby, we contribute to a more holistic understanding of human-CA interaction and provide a starting point for future research. The findings can further guide practitioners regarding which factors to consider in their decisions when investing in CAs.
Introduction to the Minitrack on Artificial Intelligence-based Assistants and Platforms
(2024-01-03) Schmidt, Rainer; Alt, Rainer; Zimmermann, Alfed

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