This publication-based dissertation examines human-related success factors for the implementation and application of data analytics tools and methods within the decision-making process of organizations. Generated insights on human-related factors are outlined and described in six chapters. First, a general introduction to the subject is provided and the research is positioned within a broader overall context. Additionally, the first section comprises a summary of the research papers included, along with publication information. Chapter 2 presents a systematic literature review summarizing the capabilities of Big Data analytics (BDA) with regard to firm performance. Five key capability clusters have been identified to categorize all relevant human-related capabilities across existing research to date. Chapter 3 presents an empirical research paper examining the relevant managerial aspects that must be considered when shifting from intuitive to analytics-based decision-making. Introducing a six-factor framework, the chapter outlines the findings of an indepth single case study of a German manufacturing organization that has already implemented analytical methods and tools within its decision processes. Chapter 4 contains the second empirical paper, which outlines the crucial role that executives play within the process of a firm’s digital transformation toward the application of analytics. Based on conducted interviews, four managerial archetypes are identified, with detailed descriptions of their characteristics, capabilities, and contribution to transformation. Chapter 5 introduces a teaching case study that sheds light on best practices relevant to the application of analytics. This case study describes the most critical factors for success in the use of an AI tool using an example from Wilo, a leading German manufacturer of pumps and pump systems. Finally, Chapter 6 summarizes the findings of this publication-based dissertation, outlines its contributions to academia and practice, and presents its limitations and potential avenues for future research.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:84908 |
| Date | 24 April 2023 |
| Creators | Korherr, Philipp |
| Contributors | Kanbach, Dominik K., Stubner, Stephan, HHL Leipzig Graduate School of Management |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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