Non-verbal evaluation of retail service encounters through consumers’ facial expressions

Emotions have been largely acknowledged as important drivers of many consumers’ behaviors. They are usually recognized through particular facial expressions, body language and gesture. However, the increasing integration of automatic systems in retailing is pushing researchers to understand the extent to which these systems can support employees to better understand consumers’ shopping experience. In this vein, the present research aims at investigating the extent to which it is possible to systematically evaluate retail service encounters through consumers’ facial expression. To this end, the research provides a machine learning algorithm to detect the six fundamental (human) emotions based on facial expressions associated with consumers’ shopping experience in the 19 biggest shopping centers in UK, and (ii) investigates consumers’ response to the usage of this system to automatically collect their evaluation of the retail service encounters. Findings reveal that a facial expression recognition system would uncover consumers’ evaluation of retail service encounters, and that consumers would accept the usage of facial expression identification systems to automatically evaluate the retail service encounters.