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Artificial Intelligence Method for the Analysis of Marketing Scientific Literature
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Author(s): Antonio Hyder (Miguel Hernandez University, Spain & Hackers and Founders Research, USA), Carlos Perez-Vidal (Universidad Miguel Hernandez, Spain)and Ronjon Nag (Stanford University, USA)
Copyright: 2023
Pages: 18
Source title:
Philosophy of Artificial Intelligence and Its Place in Society
Source Author(s)/Editor(s): Luiz Moutinho (University of Suffolk, UK), Luís Cavique (Universidade Aberta, Portugal)and Enrique Bigné (Universitat de València, Spain)
DOI: 10.4018/978-1-6684-9591-9.ch008
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Abstract
A machine-based research reading methodology specific to the academic discipline of marketing science is introduced, focused on the text mining of scientific texts, analysis and predictive writing, by adopting artificial intelligence developments from other research fields in particular materials and chemical science. It is described how marketing research can be extracted from documents, classified and tokenised in individual words. This is conducted by applying text-mining with named entity recognition together with entity normalisation for large-scale information extraction of published scientific literature. Both a generic methodology for overall marketing science analysis as well as a narrowed-down contextualised method for delimited marketing topics are detailed. Automated literature review is discussed as well as potential automated formulation of hypotheses and how AI can assist in the transfer of marketing research knowledge to practice, in particular to startups, as they can benefit from AI powered science-based decision making. Recommendations for next steps are made.
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