Realizing Business Value Through Artificial Intelligence-Driven Analytics: Theoretical Foundation and Empirical Evidence
Please use this identifier to cite or link to this item:
https://doi.org/10.48693/164
https://doi.org/10.48693/164
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Prof. Dr. Frank Teuteberg | ger |
| dc.creator | Anton, Eduard | - |
| dc.date.accessioned | 2022-08-26T11:04:07Z | - |
| dc.date.available | 2022-08-26T11:04:07Z | - |
| dc.date.issued | 2022-08-26T11:04:08Z | - |
| dc.identifier.uri | https://doi.org/10.48693/164 | - |
| dc.identifier.uri | https://osnadocs.ub.uni-osnabrueck.de/handle/ds-202208267324 | - |
| dc.description.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. | eng |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Germany | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/de/ | * |
| dc.subject | Business Value | eng |
| dc.subject | Artificial Intelligence | eng |
| dc.subject | Analytics | eng |
| dc.subject | Capabilities | eng |
| dc.subject.ddc | 330 - Wirtschaft | ger |
| dc.subject.ddc | 650 - Management | ger |
| dc.title | Realizing Business Value Through Artificial Intelligence-Driven Analytics: Theoretical Foundation and Empirical Evidence | eng |
| dc.type | Dissertation oder Habilitation [doctoralThesis] | - |
| thesis.location | Osnabrück | - |
| thesis.institution | Universität | - |
| thesis.type | Dissertation [thesis.doctoral] | - |
| thesis.date | 2022-06-21 | - |
| orcid.creator | https://orcid.org/0000-0002-5676-710X | - |
| dc.contributor.referee | Prof. Dr. Oliver Thomas | ger |
| dc.subject.bk | 85.20 - Betriebliche Information und Kommunikation | ger |
| Appears in Collections: | FB09 - E-Dissertationen | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| thesis_anton.pdf | Präsentationsformat | 1,46 MB | Adobe PDF | thesis_anton.pdf  View/Open |
This item is licensed under a
Creative Commons License
