Title:	Realizing Business Value Through Artificial Intelligence-Driven Analytics: Theoretical Foundation and Empirical Evidence
Authors:	Anton, Eduard
ORCID of the author:	https://orcid.org/0000-0002-5676-710X
Thesis advisor:	Prof. Dr. Frank Teuteberg
Thesis referee:	Prof. Dr. Oliver Thomas
Abstract:	Enabling data-driven decision-making is a central theme of modern digital strategies. In this context, expectations are often placed on artificial intelligence (AI)-driven analytics to exploit the unutilized information potential of big data. However, companies regularly fall short of their expectations, as they lack the knowledge of building AI capabilities or the understanding of value-creating mechanisms. The overarching aim of this cumulative dissertation is to provide theoretical underpinnings for and empirical evidence of the mechanisms necessary to build and realize AI capabilities for data-driven value creation in an organizational context. To attain the overarching research objective, this cumulative dissertation reports on eight individual research papers embedded in a framework that builds on the big data analytics-related business value model of Grover et al. (2018). The research contributions draw on a wide range of qualitative and quantitative methods, addressing behavioral and design-oriented research questions in the field of information systems.
URL:	https://doi.org/10.48693/164 https://osnadocs.ub.uni-osnabrueck.de/handle/ds-202208267324
Subject Keywords:	Business Value; Artificial Intelligence; Analytics; Capabilities
Issue Date:	26-Aug-2022
License name:	Attribution-NonCommercial-NoDerivs 3.0 Germany
License url:	http://creativecommons.org/licenses/by-nc-nd/3.0/de/
Type of publication:	Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:	FB09 - E-Dissertationen

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