Computerized content analysis of online data – opportunities for marketing scholars and practitioners. (27th February 2020)

• Record Type: Journal Article
• Authors: Kietzmann, Jan; Pitt, Leyland F.
• Is Part Of: European journal of marketing. Volume 54:Number 3(2020)
• Page Start: 473
• Page End: 477
• Publication Date: 2020-02-27
• Subjects: Marketing -- Text analysis -- Content analysis -- Artificial intelligence -- Consumer research -- Machine learning -- LIWC -- DICTION -- IBM Watson -- CATA -- Automated text analysis; Marketing -- Periodicals; Consumer behavior -- Periodicals; 658.8
• DOI: 10.1108/EJM-01-2020-0007
• Languages: English
• ISSNs: 0309-0566

Abstract

Abstract : Purpose: The purpose of this paper is to summarize the main developments from the early days of manual content analysis to the adoption of computer-assisted content analysis and the emerging artificial intelligence (AI)-supported ways to analyze content (primarily text) in marketing and consumer research. A further aim is to outline the many opportunities these new methods offer to marketing scholars and practitioners facing new types of data. Design/methodology/approach: This conceptual paper maps our methods used for content analysis in marketing and consumer research. Findings: This paper concludes that many new and emerging forms of unstructured data provide a wealth of insight that is neglected by existing content analysis methods. The main findings of this paper support the fact that emerging methods of making sense of such consumer data will take us beyond text and eventually lead to the adoption of AI-supported tools for all types of content and media. Originality/value: This paper provides a broad summary of nearly five decades of content analysis in consumer and marketing research. It concludes that, much like in the past, today's research focuses on the producers of the words than the words themselves and urges researchers to use AI and machine learning to extract meaning and value from the oceans of text and other content generated by organizations and their customers.