The Factor Analysis Impact Maps (FAIM) visualisation approach was developed through a university-industry collaboration project. The main objective was to build a simple, interactive alternative to traditional rating scales, which should also carry the ‘personal visualisation (PV)’ function. Besides being useful for the data analyst, this feature engages respondents. Survey-takers actively get involved in the research activity by building visual representations of their responses.

PV is an emerging research methodology. After analysing the explanations offered by Pousman et al. (2007) and Huang et al. (2015), we define PV as ‘the design of interactive, visual data representation, to depict personally meaningful information which is also of value to the research participant’. Respondents, by answering the interviewer’s questions, create a visual representation of their responses. PV techniques are useful as these help the research participants in identifying and highlighting key elements in their environment which may lead to particular phenomena (McCarty et al., 2007).

FAIM uses a computerised ‘dartboard’ with a centre, surrounded by 10 concentric circles as part of an online survey questionnaire. Each circle is assigned a number that indicates the distance from the centre. The centre of the image is labelled with a 10. Issues that matter the most should be at the centre of the final image, representing great importance (10 means ‘extremely high influence or impact’). As one moves away from the centre, the number decreases. Any factors that are perceived irrelevant (or of no significance) can be placed in a ‘trash bin’. Factors left at the bin are assigned a value of zero. As a respondent gets ready to take the survey, a set of potential items - or factors- are visible at the outer region of the dartboard. These items are represented by a sphere. With the help of a cursor or a touch screen, the respondent moves each of the spheres as close to or as further away from the centre as is needed. Once the survey is complete, the final image appears as a spider-web, with the core factors easily highlighted for the respondent.

The FAIM tool has been tested and validated with small business owners (Blamey and Holmes 2014). In comparison to other scales, FAIM generated more carefully-selected responses and a lower proportion of ‘straight-lining’ (Gupta and Holmes 2015). We propose that tools like FAIM, which make PV possible, provide greater value to survey-takers. At a time when academics and practitioners are discussing ways of enhancing respondent-engagement, the concept of PV in surveys needs to be further explored.