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• W3183328376 startingPage "1179" @default.
• W3183328376 abstract "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 the 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." @default.
• W3183328376 created "2021-08-02" @default.
• W3183328376 creator A5004737947 @default.
• W3183328376 creator A5038520512 @default.
• W3183328376 creator A5059883724 @default.
• W3183328376 date "2021-07-21" @default.
• W3183328376 modified "2023-10-18" @default.
• W3183328376 title "Retail Managers’ Preparedness to Capture Customers’ Emotions: A New Synergistic Framework to Exploit Unstructured Data with New Analytics" @default.
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