Tackling social media data analysis: Comparing and contrasting QSR NVivo and Leximancer

Research output: Contribution to journalArticle

1 Citation (Scopus)
124 Downloads (Pure)

Abstract

Purpose: This paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication. Design/methodology/approach: This study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented. Findings: This research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA. Research limitations/implications: Qualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research. Practical implications: The paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data. Originality/value: This paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

Original languageEnglish
Pages (from-to)94-113
Number of pages20
JournalQualitative Market Research
Volume22
Issue number2
DOIs
Publication statusPublished - 8 Apr 2019

    Fingerprint

Cite this