Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

David Aufreiter (), Doris Ehrlinger (), Christian Stadlmann (), Margarethe Überwimmer (), Anna Biedersberger (), Christina Korter () and Stefan Mang ()
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David Aufreiter: Upper Austria University of Applied Sciences, Steyr, Austria
Doris Ehrlinger: Upper Austria University of Applied Sciences, Steyr, Austria
Christian Stadlmann: Upper Austria University of Applied Sciences, Steyr, Austria
Margarethe Überwimmer: Upper Austria University of Applied Sciences, Steyr, Austria
Anna Biedersberger: Centre of Market Research of the University of Passau, Neuburg am Inn, Germany
Christina Korter: Centre of Market Research of the University of Passau, Neuburg am Inn, Germany
Stefan Mang: Centre of Market Research of the University of Passau, Neuburg am Inn, Germany

Marketing Science & Inspirations, 2021, vol. 16, issue 2, 2-11

Abstract: On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

Keywords: export; artificial intelligence; servitization; manufacturing companies (search for similar items in EconPapers)
JEL-codes: M16 M31 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:cub:journm:v:16:y:2021:i:2:p:2-11

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