To read this content please select one of the options below:

Making sense of text: artificial intelligence-enabled content analysis

Linda W. Lee (Nottingham Business School, Nottingham Trent University, Nottingham, UK)
Amir Dabirian (Department of Industrial Marketing, KTH Royal Institute of Technology, Stockholm, Sweden and Mihaylo College of Business and Economics, California State University Fullerton, Fullerton, California, USA)
Ian P. McCarthy (Business School, Simon Fraser University, Vancouver, Canada and Department of Management, Luiss University, Rome, Italy)
Jan Kietzmann (Gustavson School of Business, University of Victoria, Victoria, Canada)

European Journal of Marketing

ISSN: 0309-0566

Article publication date: 25 February 2020

Issue publication date: 5 March 2020




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.


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.


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.


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.



Lee, L.W., Dabirian, A., McCarthy, I.P. and Kietzmann, J. (2020), "Making sense of text: artificial intelligence-enabled content analysis", European Journal of Marketing, Vol. 54 No. 3, pp. 615-644.



Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

Related articles