Big Data has been proclaimed a revolution that will transform how we live, work, and think. It has been used to forecast everything from stock market developments to HIV epidemiology and, of course, consumer behaviour. This trend has driven marketing scholars as well as those from a range of other disciplines to investigate research questions such as the correlations be-tween Online Consumer Reviews (OCR)s and sales figures, gender differences in writing or perceiving OCRs, and even racist remarks evident in these short reviews. However, while an-swers to these questions tend to be sought by analysing written OCRs, there is no consensus or, indeed, much research about how best to analyse this kind of text. In particular there appears to be an elephant in the room: Are computerised text analyses a suitable substitution for human analysis of this sort of data? This working paper presents systematic review of recent publica-tions using either human or computerised content analyses in order to firstly, identify and map the many specific research approaches that are commonly used in different research disciplines in the field of Big Data and secondly, to examine and compare forms of content analysis by ex-plaining what their limitations and opportunities are.
|Title of host publication||Academy of Marketing Conference 2019|
|Subtitle of host publication||52nd Annual Conference Proceedings|
|Place of Publication||London|
|Publication status||Published - 2 Jul 2019|