IT should be notified now!

Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.

Donate to SABA Capital Campaign
Portal Access Behavior Analysis Training Directory Contact the Hotline View Frequently Asked Question
ABAI Facebook Page Follow us on Twitter LinkedIn LinkedIn

Ninth International Conference; Paris, France; 2017

Event Details

Previous Page


Symposium #13
Consumer Behavior Analysis Using Digital Technology
Tuesday, November 14, 2017
2:00 PM–3:50 PM
Studio F, Niveau 2
Chair: Gordon R. Foxall (Cardiff University)
Discussant: Valdimar Sigurdsson (Reykjavik University)
Abstract: Consumer behavior analysis draws on behavior analysis, behavioral ecology, behavioral economics, and marketing science to further enhance the understanding of all aspects of consumption. New technologies such as in-store analytics, improved eye tracking, customer feedback software tools and targeted, measurable, and interactive digital media are not only changing the face of the retail landscape but are also a relatively untapped opportunity in this discipline. Current activities in the digital marketplace are generating immense amounts of techniques, tools and behavioral data that can offer possibilities for more detailed analyses of consumer-marketing relationships from a behavior analytical viewpoint. In this symposium we will discuss recent theoretical developments and empirical analyses related to how consumers learn to adapt to highly competitive economic environments. The symposium starts with a theoretical paper on the relevance of consumer behavior analysis to digital marketing in the context of the Behavioral Perspective Model. The second paper explores the use of digital technology in scrutinizing in-store customer foraging in its natural surroundings focusing on key customer touch-points. The third study investigates the impact of online customer ratings on online hotel booking using the concept of probability discounting. The final paper uses behavioral conjoint as a methodology to study consumer trade-offs and reinforcement value maximization in online retailing.
Instruction Level: Advanced
Keyword(s): Behavior Analysis, Consumer Behavior, Digital Technology
Consumer Behaviour Analysis and its Relevance to Digital Marketing
GORDON R. FOXALL (Cardiff University), Vishnu Menon (Reykjavik University), Valdimar Sigurdsson (Reykjavik University), Asle Fagerstrøm (Westerdals Oslo School of Arts, Communication and Technology), Nils Larsen (UiT The Arctic University of Norway)
Abstract: The rapidly changing digital environment has redefined the way most companies interact with their customers. With consumers increasingly adopting digital technologies, a successful digital marketing strategy requires a thorough understanding of how and why consumers behave as they do. The purpose of the study is to explore the possibility of understanding digital consumer choice from a behavioral perspective and its impact for behavior analysts and digital marketers in identifying digital environmental contingencies. Furthermore, the study elaborates on the possibilities of analyzing digital consumer behavior in the context of the Behavioral Perspective Model (BPM) (Foxall, 1990/2004), examining the influence of the digital environment on consumer behavior and how such behavior can be observed, measured, analyzed, and applied to aid the understanding of digital marketing and help behavior analysts to make informed decisions.
The Use of Technology to Study Important In-Store Customer Touch-Points: Advancing Conceptualisation, Methodology and Application in Consumer Behavior Analysis
(Applied Research)
VALDIMAR SIGURDSSON (Reykjavik University), Nils Larsen (UIT-The Arctic University of Norway)
Abstract: The paper discusses the need for a thorough understanding of consumer choice of a product carrying equipment (e.g. cart, basket or nothing) as a key customer touch-point at the beginning of a customer journey in grocery retailing. A four-term contingency framework with conversion rate modeling was used, and the data consisted of actual choice behavior detected through video-surveillance. In-store antecedents such as consumers' shopping goals and carrying equipment positioning were analyzed and manipulated while random consumers were observed individually from the point they entered the store to all the way to their exit. The measurements involved consequences of different in-store antecedents (goals and equipment) on in-store behaviors such as walking speed, number of purchases per minute, time spent on different zones (e.g., in the fruit section) and the proportion of healthy food in the total shopping. The data was analyzed using a Shopper Flow Tracking System where the software is designed both to give automatic data on shopper behavior and to assist human observers in tracking individual shopping trips. We discuss behavioral classifications, methodology, validity and implications related to the data from the consumer tracking efforts.
On the Impact of Customer Ratings on Online Hotel Bookings
(Applied Research)
ASLE FAGERSTRØM (Westerdals Oslo School of Arts, Communication and Technology), Lars Syndnes (Westerdals Oslo School of Arts, Communication and Technology), Georghita Ghinea (School of Information Systems, Computing and Mathematics, Brunel University )
Abstract: This study uses the concept of probability discounting to investigate the impact from online customer ratings on online hotel bookings. Probability discounting describes how the subjective value of an outcome alters when its delivery shifts from certain to uncertain. In a simulated online scenario, 50 participants were asked to book a hotel accommodation from one of two hotel alternatives. One of the hotels had ratings from previous guests, and varied in price, while the other hotel had a set price at market price. A titration procedure over price for the hotel with customer ratings was run over seven probability conditions. Results supports previous findings, and suggests that online customer ratings indicates the probability of a successful transaction and function as a “guide” to choices. The results are discussed in relation to the concept of probability discounting. Managerial implications and suggestions for further research are given.
Behavioural Conjoint Analysis in Digital Settings
(Applied Research)
VISHNU MENON (Reykjavik University), Valdimar Sigurdsson (Reykjavik University), Asle Fagerstrøm (Westerdals Oslo School of Arts, Communication and Technology)
Abstract: The impact of subtle environmental factors on choice can be understood through the prism of behavioral economics, whereas the variations of marketing attributes and impact on choice can be measured using conjoint analysis. Using a behavioral conjoint approach, we conducted several reinforcement value maximization and trade-off analyses in an online healthy food retail setting to understand consumers’ willingness to buy both “healthy” and “unhealthy” food items, which were built on different attributes with different consequences for consumers. We overlay a monadic experiment on top of a conjoint study to exploit the advantages of both approaches. The research design compares different classifications within the same product class (healthy vs. unhealthy), different product class (food vs. fashion items), as well as diverse online platforms (e-commerce, email and social media). Results are presented in terms of partial utility scores from individual consumers based on altered interventions for consumer choices with scenario testing and demand curves. The paper provides practical implications of conjoint analysis as an experimental survey technique for decision making in different digital environments, especially related to the promotion and sales of healthy food. The role of behavioural conjoint analysis as an efficient pre-testing tool for more direct measures on behavior in online experimental analysis are also discussed.



Back to Top
Modifed by Eddie Soh