An fNIRS-based investigation of visual merchandising displays for fashion stores

Abstract

This paper investigates a brain-based approach for visual merchandising display (VMD) in fashion stores. In marketing, VMD has become a research topic of interest. However, VMD research using brain activation information is rare. We examine the hemodynamic responses (HRs) in the prefrontal cortex (PFC) using functional near-infrared spectroscopy (fNIRS) while positive/negative displays of four stores (menswear, womenswear, underwear, and sportswear) are shown to 20 subjects. As features for classifying the HRs, the mean, variance, peak, skewness, kurtosis, t-value, and slope of the signals for a 20-sec time window for the activated channels are analyzed. Linear discriminant analysis is used for classifying two-class (positive and negative displays) and four-class (four fashion stores) models. PFC brain activation maps based on t-values depicting the data from the 16 channels are provided. In the two-class classification, the underwear store had the highest average classification result of 67.04%, whereas the menswear store had the lowest value of 64.15%. Men’s classification accuracy for the underwear stores with positive and negative displays was 71.38%, whereas the highest classification accuracy obtained by women for womenswear stores was 73%. The average accuracy over the 20 subjects for positive displays was 50.68%, while that of negative displays was 51.07%. Therefore, these findings suggest that human brain activation is involved in the evaluation of the fashion store displays. It is concluded that fNIRS can be used as a brain-based tool in the evaluation of fashion stores in a daily life environment.

Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale – A fNIRS Approach

“…Although, the brain regions of reward evaluation system, especially medial OFC, were again highlighted as the driving force for the prediction, a decreased neural activity in the dlPFC was integrated in the formula to predict sales, an aspect that represents reduced cognitive effort and greater cortical relief (MacPherson et al, 2002;Carter and van Veen, 2007;Cho et al, 2010;Izuma et al, 2010;Bartra et al, 2013). Building on previous research, which demonstrated that mobile fNIRS is particularly capable of measuring neural cortical activity, especially lateral areas of the prefrontal cortex (Krampe et al, 2018a;Liu et al, 2018), the investigation of the neural signatures of the dlPFC's deactivation might be a fruitful avenue to predict the success of merchandising elements. While doing so, this research work opens up the potential application of mobile fNIRS in a realistic shopping environment, namely the PoS, to predict success on market level.…”

“…The valid application of mobile fNIRS in the field of consumer and shopper neuroscience has been demonstrated in several studies (Kopton and Kenning, 2014;Çakir et al, 2018;Krampe et al, 2018a,b). Most of the consumer neuroscience research using fNIRS focussed on the identification of neural correlates associated with merchandising in virtual in-store settings (Krampe et al, 2018b;Liu et al, 2018) or used fNIRS measurements to predict individual food-choice behaviour (Çakir et al, 2018). A recent fNIRS study conducted by Cha et al (2019) correlated neural activation patterns of the mPFC to online popularity of pop music on YouTube, presenting an extension of earlier studies that predicted music popularity in the field of consumer neuroscience applying fMRI (Berns and Moore, 2012).…”

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

“…In this study, ΔHbO and ΔHbR throughout the whole experiment were obtained from the raw optical density using the modified Bear-Lambert Law [53,54]. A fourth-order Butterworth low-and high-pass filter with a cut-off frequency of 0.0018-0.15 Hz was applied to remove cardiac noise (~1.1 Hz), respiration (0.25 Hz), and other physiological noise [55][56][57]. In addition, a detrending algorithm was used to remove shifts in the baseline.…”

Is consumer neural response to visual merchandising types different depending on their fashion involvement?

“…Over the last two decades, researchers in the social cognition field have tried to identify the effects of environmental clues on human behavior [19][20][21][22][23]. One such noteworthy theory is the "perception-behavior link," which states that a given perception about the social environment or stimuli can make people behave accordingly [24,25].…”