Making sense of text: artificial intelligence-enabled content analysis

Lee, L. ORCID: 0000-0002-3818-4445, Dabirian, A., McCarthy, I. and Kietzmann, J., 2020. Making sense of text: artificial intelligence-enabled content analysis. European Journal of Marketing, 54 (3), pp. 615-644. ISSN 0309-0566

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Abstract

Purpose: The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis.

Design/methodology/approach: To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency.

Findings: Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

Research limitations/implications: This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches.

Practical implications: For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines.

Originality/value: To the best of the authors' knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

Item Type: Journal article
Publication Title: European Journal of Marketing
Creators: Lee, L., Dabirian, A., McCarthy, I. and Kietzmann, J.
Publisher: Emerald
Date: 2020
Volume: 54
Number: 3
ISSN: 0309-0566
Identifiers:
NumberType
10.1108/EJM-02-2019-0219DOI
1120646Other
Divisions: Schools > Nottingham Business School
Depositing User: Jonathan Gallacher
Date Added: 01 Oct 2019 11:00
Last Modified: 06 Apr 2020 14:27
URI: http://irep.ntu.ac.uk/id/eprint/37891

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