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Understanding the usability of retail fashion brand chatbots: Evidence from customer expectations and experiences

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  • Aslam, Usman

Abstract

The existing literature suggests that chatbots tend to provide automated and generic responses that may not fully address complex customer inquiries, particularly when additional guidance is required. To improve the usability of retail fashion brands, this study examines how socially constructed experiences and expectations influence customer interactions. Data was collected from various sources, including online reviews, semi-structured interviews, and focus group discussions, to enhance the credibility of the results. The research reveals that individuals in the retail industry demand a broader variety of fashion products and more sophisticated capabilities to bolster the reminiscence of their shopping experiences. Retail fashion chatbots hold potential in recommending items that align with customers' preferences and goals, yet it is crucial for developers and brand managers to address numerous usability challenges and provide multilingual assistance to boost user engagement. Moreover, optimizing fashion retail chatbots is vital to reduce battery consumption, curtail instances of conversation window freezing, and hasten response times. This study provides a novel research framework that offers valuable insights on how to enhance the retail customer experience by improving interactivity, compatibility, credibility, and other factors that promote the use and adoption of retail fashion chatbots.

Suggested Citation

  • Aslam, Usman, 2023. "Understanding the usability of retail fashion brand chatbots: Evidence from customer expectations and experiences," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:joreco:v:74:y:2023:i:c:s0969698923001248
    DOI: 10.1016/j.jretconser.2023.103377
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    References listed on IDEAS

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    2. Rohit, Kumar & Shankar, Amit & Katiyar, Gagan & Mehrotra, Ankit & Alzeiby, Ebtesam Abdullah, 2024. "Consumer engagement in chatbots and voicebots. A multiple-experiment approach in online retailing context," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    3. Li, Tian-Ge & Zhang, Chu-Bing & Chang, Ying & Zheng, Wei, 2024. "The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).

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