Virtual Fit Interfaces - NVivo 12 Codebook
We collected 79 screenshots exploring the customer journeys through the different VFIs; nine journey steps per platform on average. To make sure reliability, we used a standardised, set of body measurements while exploring options for each Virtual Fit platform; uncovering size and fit recommendations. For this paper, the standardised body is an Alvanon™ body form . Dress Form size code UMR-WMSK12H-1504, dress form code AVF58535. To make sure the body form’s measurements are correct, we scanned the body form using a size stream 3D Body Scanner (SizeStream, 2017); a technology shown to be reliable for scientific research (Parker et al., 2017).
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To address Objective 1 (understanding the information required from consumers by VFIs) content analysis focused on the consumer’s self-reported assessment on body dimensions; providing key measurements and circumferences used by VFIs to establish size recommendations. Coding focuses on how VFIs collect garment data. Virtual fit references extensive database of branded products, using the specific details of the garment dimensions governed by its style. To address Objective 2 (understanding the outputs as presented by VFIs) content analysis focuses on how platforms present size and fit recommendations to the customer. This provides a variety of ways to engage with the consumer during the online shopping experience with the different interfaces requiring differing levels of detail to offer the size prediction. To address Objective 3 (testing how VFIs determine size and fit recommendations) content analysis focuses on Gill’s classification models (2015a). This basic framework provides a tool for interpretation and contextualisation of different ways the consumer positions themselves in the garment selection process.