AI, Organizing, and Management
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ItemThe Effects of Machine-powered Platform Governance: An Empirical Study of Content Moderation( 2022-01-04)With increasing participation in social media and online communities, content moderation has become an important part of the online experience. Volunteer moderators have been the essential workforce for platform governance. Recently, platforms move toward the technical and automated mode of governance. There is a growing concern over de-humanization and whether machines would lead volunteer moderators to reduce their contributions. We conduct an empirical study to examine the impact of machine-powered regulations on volunteer moderators’ behaviors. With data collected from 156 subreddits on Reddit, we found that delegating moderation to machines augments volunteer moderators’ role as community managers. Human moderators engage in more moderation-related activities, including 20.2% more corrective and 14.9% supportive activities with their community members. Importantly, the effect manifests primarily among communities with large user bases and detailed guidelines, suggesting that community needs for moderation are the key factors driving more voluntary contributions in the presence of bot moderators.
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ItemStructuring the Quest for Strategic Alignment of Artificial Intelligence (AI): A Taxonomy of the Organizational Business Value of AI Use Cases( 2022-01-04)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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ItemIncorporating Stakeholder enfranchisement, Risks, Gains, and AI decisions in AI Governance Framework( 2022-01-04)The emergence of AI-enabled applications has drawn attention to the need for AI governance. This essay builds on organizational governance literature and proposes a framework for developing organizational governance structures. Following a call to incorporate all stakeholders in governance [1], the framework considers the interests of all organizational stakeholder groups. In addition, it delineates five types of AI-related organizational decisions, which have the potential to significantly impact stakeholder interests. Furthermore, the framework considers four distinct outcomes and byproducts of AI which may impact the distribution of stakeholder benefits and risks. These need to be specifically addressed by organizational AI governance structures. We contend that the details furnished by the framework pave the way for future research on AI governance, adaptation in an AI-driven organization, and AI-related legal framework development.
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ItemChatbot Introduction and Operation in Enterprises – A Design Science Research-based Structured Procedure Model for Chatbot Projects( 2022-01-04)Chatbot research has become an emerging research area. Researchers survey the technology behind and the whole ecosystem from different perspectives, e.g., human-computer interaction, design research, or anthropomorphism. To foster the transfer from research to practice, a comprehensive structured procedure model is missing yet. Due to this, the transfer of the research results into real-world settings in enterprises is often complicated. Hereto, we propose a comprehensive structured procedure model to guide practitioners in chatbot projects based on a Design Science Research study. In doing so, necessary project steps are pointed out and corresponding research results are highlighted to make them reusable for practice in a targeted manner. Thus, we provide structured support for chatbot projects in enterprises.
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ItemArtificial Intelligence for Managerial Information Processing and Decision-Making in the Era of Information Overload( 2022-01-04)In the big data era, managers are exposed to an increasing amount of structured and unstructured information that they must process daily to make decisions. In this context, artificial intelligence (AI) functionalities can support managerial information processing (IP), which forms the basis of managers’ decision-making. To date, little is known about the themes that managers face when integrating AI into their IP and decision-making. The present paper identifies these through three focus group interviews with managers from the financial industry, validates them through a survey and derives organizational implications. The results imply that organizations should (1) evaluate managerial IP tasks and matching AI systems, (2) (re)define roles for managers and AI systems, and (3) redesign management processes for sustainable human-AI interaction.
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ItemArtificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance( 2022-01-04)In today’s uncertain and disruptive environment, every firm in the supply chain is susceptible to disruptions that may require high levels of firm resilience. We argue that recent advances in artificial intelligence (AI) may help. This paper expands our understanding of the role of AI in shaping firm resilience to supply chain disruptions and, in turn, enhancing firm performance. In doing so, we conceptualize AI use as a dynamic information processing capability—consisting of three dimensions: coordinating/integration, learning, and strategic competitive response capability—as an antecedent of firm resilience to supply chain disruptions, and firm resilience as a mediation factor that links AI use and firm performance. By analyzing the data gathered using a two-stage survey from 107 companies in Europe, we found AI use has a direct impact on firm resilience, and firm resilience fully mediates the relationship between AI use and firm performance. The findings of this study contribute to IT and supply chain literature.
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