Consumers' Online Cognitive Scripts: A Neurophysiological Approach
Sylvain Sénécal (HEC Montréal)Pierre-Majorique Léger (HEC Montréal)
Marc Fredette (HEC Montréal)René Riedl (University of Linz)
Gmunden Retreat on NeuroIS 2012 Gmunden, Austria, June 3-6
Tech3Lab
Scientific partners
Insfrastructure financing
Operational financing
Group experimental room
Group experimental room
Individual experimental room
Individual experimental room
Cognitive Script
• “Coherent sequence of events expected by the individual, involving him either as a participant or as an observer” (Abelson 1976)
• “Predetermined, stereotyped sequence of actions that define a well-known situation.” (Shank and Abelson 1977)
• Basically, it allows people to understand and behave appropriately in a particular situation (Abelson 1981).
• Experts form more elaborate, distinctive, contingent, and hypothetical scripts than novices (Leong et al. 1989; Martin 1991)
Research Objectives
1. Validate if consumers activate cognitive scripts when shopping online;
2. Understand how cognitive scripts are formed by consumers over multiple shopping trips;
3. Investigate how consumers activating different cognitive scripts respond when facing a novel shopping environment (e.g., a new store).
Potential Contributions
• To theory:– Better understand script activation over multiple interactions
– Script and Self-service technology• To practice:– Help managers provide satisfying online experiences– Potentially, locking-in consumers with easier to learn websites (Johnson and Bellman 2003).
Cognitive Script and Neuroscience
• When encountering a situation, the brain tries to match the input information (e.g., Burger King) with a similar representation existing in memory (Barr 2009).
• By activating a certain analogy, information that is associated with this analogy in memory is triggered, generating a prediction of what to expect next (Bar 2009, Bar and Neta 2008).
• Hence, cognitive scripts could be at the basis of human brain activity and consequently human behaviors
• “Information encoded in our memory guides and sometimes dictates our future behavior. One can look at our experience as stored in memory as scripts.” (Bar 2009, p.1239)
Hypotheses
• H1: Consumers, whose script was formed during repeated visits to a single website (intrascript consumers), will possess a different script than those whose script was formed during single visits to different websites (interscript consumers).
Neurophysiological differences betweenNovices and Experts
Hill and Schneider, 2006
Hypotheses
• H2: When revisiting the website, intrascript consumers will more use automatic processing.
• H3: When visiting a new and dissimilar website, intrascript consumers will use more controlled processing.
Hypotheses
• H4: When visiting a new and dissimilar website, interscript consumers will have more positive attitudes toward the website than intrascript consumers.
Method
• Pretest conducted in Summer 2011• Main study– Lab expriment– Subjects: No online music purchase experience– Data collected in March - April 2012– Data analysis: ongoing
Method – Main study
• Questionnaire 1– « Online music purchase » script elicitation 1– List of 10 songs
• Random assignment to Group A (same site) or B (different sites)– Purchase task: One song per website (1 hour and/or 10 websites
max.)• Questionnaire 2
– « Online music purchase » script elicitation 2– Website evaluation (Bressolles and Nantel 2008)– Demographics
Experimental Design
Intrascript
Interscript
One Hour (maximum)
A A A A A A A A A A L
B C D E F G H I J K L
Questionnaire
Questionnaire
Measures
• Neurophysiological– Electroencephalography (EEG) and Heart Rate (HR)
• B-Alert® X10• EEG based engagement index (Berka 2007): sleep onset, distraction, low engagement, and high engagement.
• EEG based workload index
– Electrodermal response (EDR) • Procomp Infinity (Thought Technology)
• Self-reported– Website evaluation (Bressolles and Nantel 2008)
• Aesthetics, Ease of Use, Information Quality, Interactivity/Personalisation
Pretest and Main StudyB-Alert EEG Headset
Results
• Pre-test done in Summer 2011 with 6 subjects.• Real test done in March-April 2012 with 20
subjects. • Once the data are processed, we still have about
20 measures per subject per thousandth of second (approx. 75,000,000 observations per subject).
• Per subject, we have approx. 250Mo of data (unstructured data excluded)
Description des données
Pre Test (n=5)
achat1
achat2
achat3
achat4
achat5
achat6
achat7
achat8
achat9
achat1
0
ACHAT11
70%
75%
80%
85%
90%
95%
100%
Intrascript Condition (1 site)
High/Low Eng Sleep/Distracted
achat1
achat2
achat3
achat4
achat5
achat6
achat7
achat8
achat9
achat1
0
ACHAT11
70%
75%
80%
85%
90%
95%
100%
Interscript Condition (10 sites)
High/Low Eng Sleep/Distracted
H1 : Consumers, whose script was formed during repeated visits to a single website (intrascript consumers), will possess a different script than those whose script was formed during single visits to different websites (interscript consumers).
• Two independent judges coded each participant’s initial and final scripts (Intercoder reliability coefficient 0.97)
• Average number of events between the initial and final script:– Intrascript condition : from 5.70 to 5.65 events– Interscript condition : from from 7.18 to 7.55 events– Difference in variation (-0.05 vs. 0.37) was not statistically
significant based on a Mann-Whitney test (n1=10 and n2=11)
H2: When revisiting the website, intrascript consumers will more use automatic processing.H3: When visiting a new and dissimilar website, intrascript consumers will use more controlled processing.
Intrascript group Interscript group
N = 14
H4 : When visiting a new and dissimilar website, interscript consumers will have more positive attitudes toward the website than intrascript consumers.
N = 21
Next step
is the observed probability to be (high or low) engaged for subject i, after seconds during visit v (i=1,…,20 ; v=1,…,11); is a Gaussian random effect to account for the potential correlation within each subject; is a dummy variable indicating the group assigned to subject i;is a Gaussian error term.
0 if i≠i’;Corr(,= 0 if v≠v’;
if i=i’ and v=v’.