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Retail Managers’ Preparedness to Capture Customers’ Emotions: A New Synergistic Framework to Exploit Unstructured Data with New Analytics

Lookup NU author(s): Professor Eleftherios AlamanosORCiD

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Wiley-Blackwell Publishing Ltd, 2022.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

Although emotions have been investigated within strategic management literature from an internal perspective, managers ability and willingness to understand consumers emotions, with emphasis on retail sector, is still a scarcely explored theme in management research. The aim of this paper is to explore the match between the supply of new analytical tools and retail managers’ attitudes towards new tools to capture customers’ emotions. To this end, Study 1 uses machine learning algorithms to develop a new system to analytically detect emotional responses from customers’ static images (considering the exemplar emotions of happiness and sadness), whilst Study 2 consults management decision makers to explore the practical utility of such emotion recognition systems, finding a likely demand for a number of applications, albeit tempered by concern for ethical issues. While contributing to the retail management literature with regard to customers’ emotions and big data analytics, the findings also provide a new framework to support retail managers in using new analytics to survive and thrive in difficult times.


Publication metadata

Author(s): Pantano E, Dennis C, Alamanos E

Publication type: Article

Publication status: Published

Journal: British Journal of Management

Year: 2022

Volume: 33

Issue: 3

Pages: 1179-1199

Print publication date: 05/07/2022

Online publication date: 21/07/2021

Acceptance date: 05/07/2021

Date deposited: 09/07/2021

ISSN (print): 1045-3172

ISSN (electronic): 1467-8551

Publisher: Wiley-Blackwell Publishing Ltd

URL: https://doi.org/10.1111/1467-8551.12542

DOI: 10.1111/1467-8551.12542


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