Virtually realistic? Analysing Consumer Engagement to Interactive Online Environments: An EEG study

Virtually realistic? Analysing Consumer Engagement to Interactive Online Environments: An EEG study

Meera Dulabh, University of Manchester Dr. Delia Vazquez, University of Manchester

Dr. Daniella Ryding, University of Manchester Dr. Alex Casson, University of Manchester

Abstract

This study provides a conceptional approach within the consumer behaviour field at a subliminal level by teasing out consumer engagement concepts in marketing and neuro-marketing with online interactive environments. Online interactive elements of traditional fashion websites; social media, browsing and videos are considered (Manganari et al 2009, Manganari et al. 2011). A cognitive neuroscience technique using an Electroencephalogram (EEG) (A non-invasive

procedure measuring the brain’s electrical activity at different sites on the head) is proposed. Originality in this study stems from the application of a new theoretical framework, ability to

combine traditional and non-traditional marketing methods and addressing emerging fields of the future such virtual shopping and BCI (brain-computer-interaction) applications.

Introduction

Virtual Reality Simulators incorporating multisensory elements date back to 1962 with the Sensorama Simulator, this was a machine presented with 3D images, smells, sound, wind and vibrations (Spence & Gallace 2012). Virtual reality can is a world portrayed through a three

dimensional computerised interface imitating that of the ‘real world’ and can build brand equity through experiential service interactions (Barnes and Mattsson 2008, Barnes et al. 2015). Wang

et al. (2012) specifies that in virtual reality, 3D body models of users are created beforehand and are loaded onto the computer, whereas in augmented reality, garments are directly displayed on the body of users and not 3D ready made models. Second life is a virtual world in which allows many users to connect together via avatars (Wang & Hsu 2011). Barnes et al. (2015) report that brand presence provides low-end experiential and emotional value, hence this explains why brands such as Sears, Adidas and Armani have since then terminated their second life developments. Thus, the value in second life is not determined by branding but more by et al. (2012) specifies that in virtual reality, 3D body models of users are created beforehand and are loaded onto the computer, whereas in augmented reality, garments are directly displayed on the body of users and not 3D ready made models. Second life is a virtual world in which allows many users to connect together via avatars (Wang & Hsu 2011). Barnes et al. (2015) report that brand presence provides low-end experiential and emotional value, hence this explains why brands such as Sears, Adidas and Armani have since then terminated their second life developments. Thus, the value in second life is not determined by branding but more by

Virtual reality headsets such as the Oculus Rift allow retailers to give customers a 360-degree immersive experience; it has diverted attention from gaming to more applicable contexts (Mintel 2014b). For example, a London shopping Centre allowed shoppers to experience shopping trends by using gesture tracking technology allowing them to use their hands to transport them into another environment (Mintel 2014c). VR has also been used in clinical practices, for children inside classrooms, to overcome phobias of enclosed spaces (Spence & Gallace 2011) to treat

those with arachnophobia (Spence & Gallace 2011) and to generate “phantom limbs” (Murray et al. 2007). Both VR and AR headsets are in its developing stages, the leading AR/VR initiatives

include Samsung Gear for games and video content, the Oculus and HTC Vive for immersive gaming and Microsoft HoloLens using Augmented reality (Euro Monitor 2014b).

Literature Review

Online Interactive Shopping Environments Virtual Social Presence, Virtual Atmospherics and Virtual Theatrics Online interactivity can be seen as the extent to which the interaction between the website, consumer and another are facilitated by website features (Yoo et al.2015). Manganari et al. (2009)

devise an “Online Store Environment Presentation Framework” in which they split their research into four components; virtual layout and design, virtual atmospherics, virtual theatrics and virtual

social presence. Virtual social presence can be characterised by Electronic Word of Mouth (e- WOM) that includes positive or negative statement made by consumers about a product made available to a mass community via the internet (Henning-Thurau et al. 2004). Virtual theatrics is

a way in which retailers make their brand look like a ‘theatre’ through the use of images, video’s, graphics and animation and is seen to be more engaging than a static image (Manganari et al. 2009, Fiore and Kelly 2007). Virtual atmospherics incorporate navigation systems that enables the user to find information through links to other pages (Nielsen 1999). Websites that use an unstructured design, monotonous colours, or messy product presentations can lead consumers to feel confused and angry (Koo & Ju 2010, Okonkwo 2010).

Virtual Shopping Environments Immersive 3D store environments are seen to simulate products more closely than a 2D virtual store environment due to a richer presentation of the product and a greater level of interactivity (Jiang & Benbasat 2007). However, the threat is that utilitarian consumers will perceive vivid presentation to be less useful as content will be deemed as ambiguous (Hoch & Deighton 1989). Hedonic consumers are less likely to discriminate against ambiguous information (Gilovich et al. 2015). Wu et al (2015) focused on visual merchandising in a 3D virtual environment and

suggested that retailers shouldn’t focus on designing a merely utilitarian environment in 3D but a clear environment focusing on lifestyle rather than merchandise. Virtual shopping tasks have also

been performed to help those with disabilities. Negut et al. (2016) found that stroke patients did not complete their task efficiently in a virtual shopping task and lacked a cognitive strategy in planning compared to controls.

Theoretical Framework and Conceptional Approach In contrast to experimental or theoretical approaches, this study advocates a conceptual approach as no primary data is collected as of yet (Pang 2016). With the information presented above a theoretical frame work is proposed using the online interactive features coined from Manganari et al. (2009). Consumer engagement will be measured coined from marketing constructs

established from O’Brien & Toms (2010), Novak et al. (2000). Purchase intention constructs are established from Foxall (2004) and Bart et al. (2005). Learning history constructs are established

from Lim et al. (2015) and Demangeot & Broderick (2007). The overall structure of the framework is derived from Mehrabian & Russell (1973) stimulus-Organism-Response framework. Similar to the S-O-R Brodie et al. (2011) reviews all of consumer engagement theory and acknowledges

‘involvement’ and ‘participation’ as antecedents. Similar to fig 1. Brodie et al. (2013) uses cognitive, emotional and behavioural as engagement dimensions whereas this study used

involvement, arousal and engagement as antecedent.

Figure 1. Neuro-Engagement Theoretical Framework

Future Research Methods

Purposive sampling and convenience sampling will be used involving participants that are available to the researcher (Bryman 2011). As online shopping has grew by 11%, female fashion consumers who shop online will be investigated (Mintel 2014). Participants between 18-25 will be used for the survey, EEG and interview as they fall under ‘millennial’ consumers to which “Kids are getting older younger “(KGOY). The current technological age we are in is generation Z born in the years 1991-2002 (age 8-19) (Euromonitor International 2014a). For the time being, ASOS will be the website stimuli examined as it is considered the most interactive fashion website and houses 8 million consumers in over 200 countries (Mintel 2014a).

A separate study (Study A) will be conducted via Select Survey. Here participants will be shown an Instagram page of ASOS, be given the choice to browse a jacket on ASOS, visit a catwalk of

the jacket in ASOS and then view Youtube video’s on ASOS’s Youtube page (N=500). Data will

be analysed statistically using ANOVA and SEM modelling. The same process will be followed with an Electroencephalogram (EEG)and short interview in study 2. The Actichamp usually used for medical research has upto 160 channels and has the highest sampling frequency of 100KHz and will be the device used for this study. Oscillatory patterns of activity in the EEG range from band widths (alpha, beta, theta, delta and gamma) (Ohme & Matukin, 2012). Consumer engagement for study 2 will be measured using the algorithms (see table 2). Brain locations measured are circled in red in fig 2. these include the prefrontal cortex, Parietal and Occipital areas as these best demonstrated consumer engagement (Mc Mahan 2015).Interview data conducted after the EEG experiment will be voice recorded, transcribed and manually coded.

Figure 2. Brain Areas Associated with Engagement Source: Mc Mahan (2015)

Below is a table that demonstrates locations of engagement and the algorithms used to determine this when conducting EEG data analysis.

Table 3. Derivatives of EEG engagement

Author Type of

Explanation Engagement

EEG changes were

Increased

quantified and

Beta activity

compared with direct

when

responses of

wearing the

participants partaking

different tasks. B- Rabbi et Task

B-Alert

space suit

Alert/ AMP (Attention, al. Engagement (Alpha+Theta) sensor

memory, Profiler) (2012)

Alertness).

headset

As time

software was used as

spent

part of the EEG

increased,

wireless acquisition theta activity system. 256 samples increased.

per second

Szafir & Attention in Engagement levels

Beta/ (Alpha +

Mutlu designing

Neurosky

measured in 30

Theta)

(2012) adaptive second timeframes (2012) adaptive second timeframes

repeated measures engagement

ANOVA.

200 samples per second.1/theta out of

Adaptive Freeman

Measured in the three engagement automation

Beta/ (Alpha + BIOPAC

et al

indexes yielded the with a visual

CZ, PZ, P3

Theta)

EEF100A

highest level of tracking task engagement.

and P4

Closed loop method enables an index of

EEG was

engagement to be Pope et

recorded at

Beta/ (Alpha + identified which is al.

sites CZ, T5,

maximally sensitive to (1995)

Theta)

P3, PZ, P4,

changes in task

O1 and O2

demand

Frontal Theta

Engagement levels

Measured in

increased during Mc

Frontal

AF3, AF4,

Engagement Theta/Parietal death events Mahan

Emotiv

F3, F4, F7,

compared to general (2015)

in Gaming

Alpha

F8, FC5 &

game play events