Shifting ML value creation mechanisms : A process model of ML value creation




Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Shollo, Arisa; Hopf, Konstantin  ; Thiess, Tiemo; Müller, Oliver
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 20
Source/Other editions: The journal of strategic information systems, 31 (2022), 3, 20 S. - ISSN: 1873-1198
is version of: 10.1016/j.jsis.2022.101734
Year of first publication: 2022
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-555921
Abstract: 
Advancements in artificial intelligence (AI) technologies are rapidly changing the competitive landscape. In the search for an appropriate strategic response, firms are currently engaging in a large variety of AI projects. However, recent studies suggest that many companies are falling short in creating tangible business value through AI. As the current scientific body of knowledge lacks empirically-grounded research studies for explaining this phenomenon, we conducted an exploratory interview study focusing on 56 applications of machine learning (ML) in 29 different companies. Through an inductive qualitative analysis, we uncover three broad types and five subtypes of ML value creation mechanisms, identify necessary but not sufficient conditions for successfully leveraging them, and observe that organizations, in their efforts to create value, dynamically shift from one ML value creation mechanism to another by reconfiguring their ML applications (i.e., the shifting practice). We synthesize these findings into a process model of ML value creation, which illustrates how organizations engage in (resource) orchestration by shifting between ML value creation mechanisms as their capabilities evolve and business conditions change. Our model provides an alternative explanation for the current high failure rate of ML projects.
GND Keywords: Künstliche Intelligenz; Maschinelles Lernen; Wertschöpfung; Wissensproduktion; Automation; Data Augmentation; Strategie; Experteninterview
Keywords: Artificial intelligence (AI), machine learning (ML), Value creation mechanisms, Knowledge creation, Automation, Augmentation, AI strategy, Interview study
DDC Classification: 004 Computer science  
330 Economics  
RVK Classification: ST 515   
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/55592
Release Date: 22. September 2022

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