Elapsed time on first buying triggers brand choices within a category: A virtual reality-based study

Abstract This study integrates neuroscientific tools such as data from eye movements, store navigation, and brand choice in a virtual supermarket into a single source data analysis to examine consumer choice, customer experience, and shopping behavior in a store. Through qualitative comparative analysis, the findings suggest that a high level of attention to a brand and slow eye movements between brands lead to additional brand purchases within the product category. This study points out that the key driver of additional brand choices is the time buyers spend on the first choice, showing that the allocation of less for the first choice triggers additional purchases within the product category and, therefore, increases sales. In addition, this study discusses practical and methodological implications for retailers, manufacturers, and researchers.

[1]  Arch G. Woodside,et al.  Proposing a new logic for data analysis in marketing and consumer behavior: case study research of large-N survey data for estimating algorithms that accurately profile X (extremely high-use) consumers , 2012 .

[2]  Sanjog Misra,et al.  Disentangling Preferences and Learning in Brand Choice Models , 2012, Mark. Sci..

[3]  J. Walkup.  Fuzzy-set social science , 2003 .

[4]  Arie W. Kruglanski,et al.  The dynamics of consumer behavior: A goal systemic perspective , 2012 .

[5]  Max A. Viergever,et al.  Do you like what you see? The role of first fixation and total fixation duration in consumer choice , 2015 .

[6]  R. Pieters,et al.  Visual attention during brand choice : The impact of time pressure and task motivation , 1999 .

[7]  Eric T. Bradlow,et al.  Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior , 2009 .

[8]  A. Woodside,et al.  Critical Tests of Multiple Theories of Cultures’ Consequences , 2013 .

[9]  K. Rayner.  Eye movements in reading and information processing: 20 years of research. , 1998, Psychological bulletin.

[10]  Michel Wedel,et al.  Looking at vision: Eye/face/head tracking of consumers for improved marketing decisions , 2014 .

[11]  Sam K. Hui,et al.  Deconstructing the “First Moment of Truth”: Understanding Unplanned Consideration and Purchase Conversion Using In-Store Video Tracking , 2013 .

[12]  Kent B. Monroe,et al.  The Effects of Time Constraints on Consumers' Judgments of Prices and Products , 2003 .

[13]  R. E. Milliman,et al.  Atmospheric Effects on Shopping Behavior: A Review of the Experimental Evidence , 2000 .

[14]  J. Jacoby,et al.  Brand Choice Behavior as a Function of Information Load , 1974 .

[15]  Carsten Q. Schneider,et al.  Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis , 2012 .

[16]  Sandra Lowe,et al.  Redesigning Social Inquiry Fuzzy Sets And Beyond , 2016 .

[17]  T. Walvis,et al.  Three laws of branding: Neuroscientific foundations of effective brand building , 2008 .

[18]  Eric T. Bradlow,et al.  Does In-Store Marketing Work? Effects of the Number and Position of Shelf Facings on Brand Attention and Evaluation at the Point of Purchase , 2009 .

[19]  Corey J. Bohil,et al.  Virtual reality in neuroscience research and therapy , 2011, Nature Reviews Neuroscience.

[20]  Richard Dewhurst,et al.  Using eye-tracking to trace a cognitive process: Gaze behavior during decision making in a natural environment , 2013 .

[21]  B. Kahn.  Consumer variety-seeking among goods and services: An integrative review , 1995 .

[22]  Eleonora Pantano,et al.  Innovation in Retail Process: From Consumers’ Experience to Immersive Store Design , 2012 .

[23]  Judy M. Vance,et al.  Comparison of Single-Wall Versus Multi-Wall Immersive Environments to Support a Virtual Shopping Experience , 2011 .

[24]  Jacob L. Orquin,et al.  Attention and choice: a review on eye movements in decision making. , 2013, Acta psychologica.

[25]  Raymond R. Burke,et al.  An Examination of Social Influence on Shopper Behavior Using Video Tracking Data , 2014 .

[26]  Björn N. S. Vlaskamp,et al.  Crowding degrades saccadic search performance , 2005, Vision Research.

[27]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[28]  J. E. Russo,et al.  An Eye-Fixation Analysis of Choice Processes for Consumer Nondurables , 1994 .

[29]  M. Just,et al.  Cognitive coordinate systems: accounts of mental rotation and individual differences in spatial ability. , 1985, Psychological review.

[30]  Eleonora Pantano,et al.  Modeling innovative points of sales through virtual and immersive technologies , 2012 .

[31]  Antonio Moreno,et al.  Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information , 2014, Manag. Sci..

[32]  Wayne D. Hoyer,et al.  Why Switch? Product Category–Level Explanations for True Variety-Seeking Behavior , 1996 .

[33]  Martin Meißner,et al.  Mobile Recommendation Agents Making Online Use of Visual Attention Information at the Point of Sale , 2013 .

[34]  Erik Wästlund,et al.  Heuristics and resource depletion: eye-tracking customers’ in situ gaze behavior in the field , 2015 .