THE END OF TRADITIONAL FOCUS GROUPS? SCALING UP QUALITATIVE RESEARCH QUICK, YET MAINTAINING DEPTH.

The key benefits of qualitative research are rich insights and thick data. However, this may come at the cost of small sample sizes and low generalisability of findings. With traditional focus group sessions (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. It was prudent to explore methods to gather thick data quickly, to effectively replace FGDs. This would increase the number of respondents in a single session, without increasing manpower or lengthening fieldwork, and maintaining data quality. This paper details the experience of running a pilot study of a 1.5-hour online discussion with N=103 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, a full qualitative study was run, with a larger sample and in the same duration required for a typical FGD. The discussion was text-based - respondents could view and give their agreement or disagreement to what others may have said without directly interacting. The data was compared to data collected from a previous study where a total of 35 respondents across 5 FGDs discussed a similar topic, and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. It has its limitations, primarily the inability to further nuance responses. Despite this, the pilot appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as large-scale engagement studies which require both breadth and scalability. [ABSTRACT FROM AUTHOR]