An added value of neuroscientific tools to

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going under headings such as “consumer neuroscience” and “neuromarketing” (Ariely and Berns,. 2010; Lee, Broderick, and Chamberlain, 2007; Plassmann, ...
An added value of neuroscientific tools to understand consumers’ in-store behaviour     Author: DALIA BAGDZIUNAITE - Email: [email protected]     University: COPENHAGEN BUSINESS SCHOOL     Track: Consumer Behaviour     Co-author(s): Khalid Nassri (Center for Decision Neuroscience, Department of Marketing, Copenhagen Business School) / Jesper Clement (Center for Decision Neuroscience, Department of Marketing, Copenhagen Business School) / Thomas Zoëga Ramsøy (Center for Decision Neuroscience, Department of Marketing, Copenhagen Business School and Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre)

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EMAC 2014 An added value of neuroscientific tools to understand consumers’ in-store behaviour

Abstract Little is still understood about the actual in-store purchase process. Here, we report that prior ad exposure affects in-store decision-making. By using mobile eye-tracking and electroencephalography (EEG), we demonstrate that, unbeknownst to participants, the ad effect was driven by increased visual exploration of the product shelves for the brand that was presented during ad exposure, and a stronger motivation score, as assess by the brain’s prefrontal asymmetry. These findings are discussed in the light of the academic and commercial need to integrate neuroscientific tools in the study of consumer perception and choice. Word count: 96. Keywords: in-store decision-making, eye-tracking, emotion, motivation, neuroscience, cognition, visual attention, priming Track the paper is intended for: Consumer Behaviour

1. Models of in-store decisions In recent years the field of consumer research has seen a tremendous shift in thought that has challenged the traditional neoclassical theory and methodology of decision-making, in particular, increasing focus on the unconscious aspects of consumer choice. Conventional decision models of fast and often repeated purchases like cue utilisation model (Olson and Jacoby, 1972) and theory of planned behaviour (Ajzen, 1991) have assumed that consumers know what they have come for, and that they will recognise products and brands when they see them, stressing overt and linear decisions. However in real world purchase situations, consumers are continuously consciously and unconsciously stimulated by multiple competing visual stimuli e.g. advertising or visual displays (Clement, 2013). Studies have long demonstrated that humans do not have unlimited time to make their decisions, thus highlighting the fundamental role of implicit and automatic nature of decision-making, in which emotionally driven physiological changes guide choices (Bechara & Damasio, 2005; Chartrand, Huber, Shiv and Tanner, 2008). Priming studies suggest that the mind can detect and process the subliminal information (Dijksterhuis, Aarts and Smiths, 2005; Fitzsimons, Chartrand and Fitzsimons, 2008), which ultimately can guide choice without awareness (Pessiglione et al., 2008). Even though not all the items in the environment reach conscious awareness, neuroscientific studies have demonstrated that attention can operate on or be directed towards an item that is not consciously perceived (Cohen, Cavanagh, Chun, and Nakayama, 2012). Several lab based studies have also shown that purchase choices can be made instantly upon product exposure, and well before subjective awareness of making such a choice, and that such choices are able to be assessed by neurobiological methods (Deppe, Schwindt, Kugel, Plassmann, and Kenning, 2005; Knutson, Rick, Wimmer, Prelec, and Loewenstein, 2007; Ramsøy, Christensen, Skov, and Stahlhut, in review; Ravaja, Somervuori, and Salminen, 2012; Tusche, Bode, and Haynes, 2010). To better assess the unconscious bases of consumer choice, many researchers are now turning to the multidisciplinary effort of combining economics, psychology and neuroscience, commonly going under headings such as “consumer neuroscience” and “neuromarketing” (Ariely and Berns, 2010; Lee, Broderick, and Chamberlain, 2007; Plassmann, Ramsøy, and Milosavljevic, 2012; Senior and Lee, 2008). However, very few studies have attempted to study consumer choice in more ecologically valid situations. Technological challenges until now have made such studies virtually impossible, however due to a synergy of advances related to hardware (approaching less intrusive and more wearable technologies), software (statistical analyses, cloud computing), and methodology (e.g., noise reduction and sophisticated multivariate modelling), researchers are now able to study consumer choice in realistic situations. The purpose of the study was to test prior advertising effects on in-store consumer behavior by employing consumer neuroscience methods to better understand responses and processes leasing up to the overt consumer choice. Our focus was drawn on visual attention, emotional responses, and motivation, addressing following hypotheses: H1: Ad exposure prior to in-store visits will be associated with a higher propensity to purchase products for brands that were shown in the ads. H2: An increased effect of ads on purchase will be undetected by participants. H3: Participants will demonstrate increased visual attention, emotional engagement and motivational responses to items bearing brands they have been previously exposed to.

2. Methodology and research design To test the hypotheses, we ran an in-store experiment in one of the leading retail home improvement and appliance stores in North America. 25 subjects (aged 40.0 ± 5.7 years; consisting of 14 women, 11 men), all right handed, without visual correction using glasses, hair extension or wigs, and no personal or family history of neurological or psychiatric conditions, were recruited from surrounding regions. Participants were instructed about the equipment and tested following the procedure abided to the Helsinki declaration1. For their participation they received $200. Subjects were randomly categorized into three groups: 1) control 2) Ad version 1 3) Ad version 2, where there were no gender (chi-square = 0.123, p=0.940) or age (F=0.03, p=0.973) differences. Data was collected performing mobile eye-tracking with Tobii Glasses 2, running at 30 Hz, and neuroimaging (electroencephalography, EEG), using a tailored version of the Emotiv EPOC 14-channel headset3, running at 128 Hz, wirelessly connected to a Samsung Galaxy Note v1 (Stopczynski, Larsen, Stahlhut, Petzersen, and Hansen, 2011; Stopczynski et al., 2013). First, devices were synchronized by holding a displayed timer on the EEG smartphone in front of the eye-tracker´s front-view camera. Then participants were mounted with the equipment and instructed to view a series of video clips including a mix of commercials and documentaries. Participants were informed to pay the attention to the sequence of video clips and that this was a part of the equipment calibration procedure. Unbeknownst to participants, one particular commercial of Brand A was shown to some of the participants. Group 1 served as a control group and only saw a standard stream of commercials and documentaries. Whereas Groups 2 and 3 were exposed to a paint commercial for Brand A halfway through the standard stream of the commercials, respectively for 15- and 30-seconds. Participants were then given a list of five tasks to be performed in the store meanwhile we recorded their visual attention (eye-tracking), as well as their emotional and motivation responses (EEG). Crucially, the middle task contained the instruction that the participant was supposed to redecorate his living room while finding, selecting and purchasing new paint. Prior to the test, participants were given $100 (in addition to the participation fee), which they could either have spent on the materials, or saved for themselves. Actual purchases were recorded during checkout, for which the participants were not instructed prior to the test. After the in-store experiment, subjects underwent a debriefing interview, where subjective aspects of the experiment were evaluated. This included 1) free associations test: asking about thoughts related to the participation; 2) category cueing: about noticing anything in particular during the test; and 3) full disclosure, where after the explanation of the ad-test, participants were explicitly requested to report their thoughts about ad effects on their in-store behaviours. Collected data was processed in Attention Tool v5.04. To synchronise eye-tracking and EEG we identified the time point from the EEG smartphone display and inserted this into the relevant time point from the eye-tracking recording. The EEG data was further preprocessed using noise reduction methods, such as ICA, allowing us to take correct for the noise of the movement and eye blinks. For each person, the video was visually inspected and relevant areas of interest (AOI) were defined in Attention Tool. Individual AOIs were drawn for shelves containing products for 1 2 3 4

http://www.wma.net/en/30publications/10policies/b3/ www.tobii.com www.emotiv.com www.imotionsglobal.com

Brand A, and from this, visual and EEG data was extracted. For assessment of the visual attention, the total fixation time that was spent looking at the AOI was calculated. To measure approach motivation, the prefrontal asymmetry of the brain was estimated using gamma frequency band, thus constructing a PAI score per shelf and per person. Higher activation on the left hemisphere relative to the right hemisphere was used as indication of approach motivation, and the opposite asymmetry was denoted as avoidance motivation (Ramsøy, Christensen, Skov, and Stahlhut, in review; Ravaja, Somervuori, and Salminen, 2012; Berkman and Lieberman, 2010; Craig, 2005; Davidson, 2004; Ochner, Green, van Steenburgh, Kounios, and Lowe, 2009).The statistical analysis was run in JMP version 9.0 (SAS Inc.), were data was analysed using a random effects model. For eye-tracking data analysis, each fixation data per AOI was used as the dependent variable and group was used as the independent variable. For the EEG analysis, the PAI score for each shelf AOI was used as the dependent variable and group as independent variable. 3. Findings Our analysis demonstrated significant main effects of prior advertising exposure on consumer choice and ad type effects on visual attention and consumer motivation, thus confirming Hypothesis 1. On average, Group 1 chose Brand A paint 78% of the times, and Groups 2 and 3 chose the product 94% of the times. The difference between groups was statistically significant (df=1, x2=4.24, p=0.034). Group 3, who saw a longer version of the commercial, chose Brand A paint 100% of the times, compared to the Group 2, seeing a shorter version of advertising and choosing Brand A paint 91.3 % of the times. However, this was not sufficient for any statistical difference (df=1, x2=1.1, p=0.304). The post-test interviews’ analysis showed that 23 out of 25 subjects did not perceive the link between the prior advertising exposure and the following in-store tasks. Furthermore, all the participants, who saw the Brand A commercial, indicated that they did in fact remember it, but that they did not believe that it had any effect on their choice of paint. Thus, our Hypothesis 2 was confirmed. In our analysis of the EEG and eye-tracking data, we found that the groups differed significantly in their visual attention towards Brand A related shelves (t=18.28, p