Analysing the effectiveness of chatbots as recommendation systems in fashion online retail: a Brazil and United Kingdom cross-cultural comparison
Analysing the effectiveness of chatbots as recommendation systems in fashion online retail: a Brazil and United Kingdom cross-cultural comparison
Chatbots as recommendation systems have become promising for online retail, enhancing the customer experience by offering a virtual shopping assistant with product suggestions. Fashion brands increasingly adopt this technology to offer personalised shopping recommendations, reduce cart abandonment, returns and complaints. This study proposes an enhanced Recommendation System that combines a simple Graphical User Interface (GUI) with the DigAI chatbot, making the customer experience more interactive and presenting more relevant clothes to the customer. The design and evaluation of the chatbot interface followed a three-phase methodology: a systematic literature review, building DigAI, and a cross-cultural fashion consumer study to evaluate the chatbot’s performance. In the study, fashion customers were to select an item from an e-commerce site both with and without the chatbot and then complete a survey questionnaire. The experiments took place in Brazil and England to identify possible cultural differences. Albeit these differences, the chatbot interface was found to provide a more satisfying user experience than the simple GUI. This effect was more pronounced among Brazilian individuals compared to those from the UK, suggesting that cultural or contextual factors may have influenced their perception of the interface improvements, which can eventually be exploited to guide fashion online retail strategies.
295-321
Landim, André R.D.B.
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Moura, J.A. Beltrao
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de Barros Costa, E.
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Vieira, T.
fea84b1f-03d0-41b6-9f3a-99b10c1b3917
Wanick Vieira, V.
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Bazaki, E.
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Medeiros, G.P.
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3 June 2025
Landim, André R.D.B.
b5b4b242-6ac3-49c8-8a30-bc0fc76127b2
Moura, J.A. Beltrao
acc70c6f-65ef-4d3a-998a-cd1231cea6e8
de Barros Costa, E.
89a2c63a-cc47-403d-aa41-867f3b88d5a3
Vieira, T.
fea84b1f-03d0-41b6-9f3a-99b10c1b3917
Wanick Vieira, V.
d2941cae-269e-4672-b448-8cb93e22e89e
Bazaki, E.
df6ddfcb-9794-48d9-95fc-f341f1d3c695
Medeiros, G.P.
0f07eb0a-18cb-4bee-a38a-b6dc8c03fe8e
Landim, André R.D.B., Moura, J.A. Beltrao, de Barros Costa, E., Vieira, T., Wanick Vieira, V., Bazaki, E. and Medeiros, G.P.
(2025)
Analysing the effectiveness of chatbots as recommendation systems in fashion online retail: a Brazil and United Kingdom cross-cultural comparison.
Journal of Global Fashion Marketing, 16, .
(doi:10.1080/20932685.2025.2491323).
Abstract
Chatbots as recommendation systems have become promising for online retail, enhancing the customer experience by offering a virtual shopping assistant with product suggestions. Fashion brands increasingly adopt this technology to offer personalised shopping recommendations, reduce cart abandonment, returns and complaints. This study proposes an enhanced Recommendation System that combines a simple Graphical User Interface (GUI) with the DigAI chatbot, making the customer experience more interactive and presenting more relevant clothes to the customer. The design and evaluation of the chatbot interface followed a three-phase methodology: a systematic literature review, building DigAI, and a cross-cultural fashion consumer study to evaluate the chatbot’s performance. In the study, fashion customers were to select an item from an e-commerce site both with and without the chatbot and then complete a survey questionnaire. The experiments took place in Brazil and England to identify possible cultural differences. Albeit these differences, the chatbot interface was found to provide a more satisfying user experience than the simple GUI. This effect was more pronounced among Brazilian individuals compared to those from the UK, suggesting that cultural or contextual factors may have influenced their perception of the interface improvements, which can eventually be exploited to guide fashion online retail strategies.
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Accepted/In Press date: 18 February 2025
e-pub ahead of print date: 3 June 2025
Published date: 3 June 2025
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Local EPrints ID: 510101
URI: http://eprints.soton.ac.uk/id/eprint/510101
PURE UUID: 7f98c534-bc18-4980-83bf-5c0bccfc3613
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Date deposited: 17 Mar 2026 18:02
Last modified: 18 Mar 2026 02:53
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Contributors
Author:
André R.D.B. Landim
Author:
J.A. Beltrao Moura
Author:
E. de Barros Costa
Author:
T. Vieira
Author:
G.P. Medeiros
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