Artificial Intelligence-based Assistants and Platforms
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Item Integrating Brand Identity Into AI-Based Conversational Agents: A Systematic Literature Review(2025-01-07) Dittmar, Angelia; Reinhard, Philipp; Li, Mahei; Karst, Fabian; Leimeister, Jan MarcoA company's brand identity is vital for fostering meaningful interactions with customers in a digital economy. As generative artificial intelligence (GenAI) becomes increasingly integrated into our daily lives, AI-based assistants are evolving into the primary channel for representing a brand's identity. Consequently, brand departments face the challenge of translating their company's brand identity into highly interactive AI-based assistants effectively. While there is diverse research on single cues that influence user perceptions, the literature lacks a comprehensive understanding of conversational cues and their impact on brand-related outcomes. Hence, we conduct a systematic literature review to identify verbal and visual cues in the design of AI-based chatbots and examine how they influence the perceptions of the assistants and the brand. We contribute a conceptual framework that summarizes the independent and dependent variables as well as their interrelationships and provides researchers and practitioners with a basis for designing and studying conversational design in the age of GenAI.Item The Impact of AI-Generated Product Summaries on the Speed and Efficiency of E-commerce Consumer Decision-Making(2025-01-07) Li, Ziru; Nie, Jialin; Zhang, Xiaofei; Shi, Michael (Zhan); Li , KaiThis research investigates how Artificial Intelligence (AI)-generated summaries influence decision-making among consumers on e-commerce platforms. By analyzing data from two Chinese digital deal platforms, Zdm and Sqkb, the study applies a Difference-in-Difference-in-Differences (DDD) methodology to evaluate how these summaries affect consumer behavior. The results demonstrate that AI-generated summaries significantly speed up consumer decision-making and reduce hesitation, with effects varying by product type and price. This research underscores the potential role of AI in enhancing consumer experiences and decision-making processes on online digital deal platforms.Item Platform Content Convergence? Investigating ChatGPT’s Impact on Airbnb Listings(2025-01-07) Schaschek, MyriamChatGPT, a generative artificial intelligence-based application, is changing content generation on digital platforms. Anecdotal evidence suggests a convergence of content with increasing writing support. Yet, rapid content convergence puts platforms' value at risk. Thus, we test for a trend of content before and after the introduction of ChatGPT. In particular, we examine linguistic indices that quantify the content characteristics. For the analyses, we use content from the Airbnb online marketplace from different time periods. Our findings suggest a significant difference in the content measured with average pairwise textual similarity. Furthermore, we identify discriminating factors for control and treatment group membership and elucidate factors contributing to textual similarity changes. We contribute to the platform governance literature by empirically testing the impact of generative AI on platform content generation and discussing its implications for platform providers' strategic considerations.Item Characteristics of Platform Shifts - A Single Case Study of ChatGPT(2025-01-07) Schmidt, Rainer; Alt, Rainer; Zimmermann, AlfedThis research investigates platform shifts as transformative change. Utilizing Yin's methodology, we examine the transition from single platforms to assistant platforms and its impact on value creation by employing ChatGPT as a single case study. The key findings indicate that the democratization of module creation, actor integration, resource integration, creation, liquefaction, and the network structure are driving the shift to assistant platforms. Platform shifts affect value creation by fostering externalities such as complementarities and network effects and enabling higher-order externalities. These encompass network effects spanning multiple platforms and complementarities connecting three or more modules. The study concludes with strategic recommendations for developers and policymakers to address these characteristics of platform shifts. It aims to enhance the theoretical understanding of platform shifts and digital ecosystems by providing an integrated perspective of the role of generative AI in shaping the technological landscape.Item Proposing the Throughput Model as Potential Algorithmic Pathways for Employing AI Machine Learning Technologies for Fraud Risk Assessments(2025-01-07) Shammakhi, Badriya; Rodgers, Waymond; Esplin, Adam; Tudor, Adriana; Yan, JieThis paper proposes and tests the Throughput Model as a potential algorithmic pathway for employing AI machine learning technologies for fraud risk assessments in auditing. The Throughput Model breaks up decision-making into four dominant concepts: Perception (P), Information (I), Judgment (J), and Decision Choice (D). This study focuses on the I→P→J→D pathway and proposes that AI machine learning algorithms following this pathway will significantly influence an auditor’s decision choice in fraud risk assessments. An exploratory study tests the effectiveness of employing I→P→J→D as a potential algorithmic pathway for fraud risk assessments. Our results show that there is a significant positive relationship between Perception and Judgment and another direct positive relation between Judgment and Decision. These findings suggest that the Throughput Model algorithms are an effective decision aid in consideration of the fraud risk factors in auditing. We also discuss the study's implications and provide guidelines for future research.Item Introduction to the Minitrack on Artificial Intelligence-based Assistants and Platforms(2025-01-07) Zimmermann, Alfed; Alt, Rainer; Schmidt, RainerItem Designing AI Assistants for Novices: Bridging Knowledge Gaps in Onboarding(2025-01-07) Holstein, Joshua; Müller, Patrick; Vössing, Michael; Fromm, HansjoergThe rapid advancements of artificial intelligence (AI) have led to its widespread adoption in enhancing productivity across various domains, including both personal productivity and workplace efficiency. Despite these advancements, the effective onboarding of novices, particularly in complex environments with extensive documentation, remains a significant challenge. Therefore, this paper explores the design of AI assistants to support novices. Utilizing a design science research approach, we collaborate with a leading pharmaceutical manufacturer to develop and evaluate an AI assistant to support novices during their onboarding. Grounded in scaffolding theory, we identify two design requirements and propose three design principles: Metadata Filtering, Graduated Complexity, and Sequential Query Generation. The evaluation of these principles demonstrates the AI assistant's effectiveness in generating accurate and contextually relevant responses, facilitating the onboarding of novices. This study provides valuable insights into the design of AI assistants, contributing to the theoretical understanding of AI-driven scaffolding and practical applications in complex industrial settings.