The user experience of voice assistants in retailing: a qualitative comparative study
Purpose This study aims to examine the user experience of voice assistants (VAs) in different retailing contexts by highlighting factors that impact the effectiveness of voice commerce services. Design/methodology/approach This study follows a qualitative research method using 30 in-depth semi-structured interviews with online shoppers (15 users of VAs from Nigeria and 15 from the UK). Following Gioia’s methodology and automated content analysis using LexiPortal, this paper examined users’ motivations for adopting VAs, their challenges and how VAs might influence customers’ brand trust and loyalty. Findings This paper found that anthropomorphism, convenience, companionship and literacy support drove shoppers’ adoption of VAs. Technophobia, audio bias, audio disparity and data security emerged as challenges facing VA users. In addition, the Nigerian participants also highlighted unreliable power supply. Despite these challenges, the participants have developed trust and personal attachment to their VAs. Originality/value This study is one of the few academic works to specifically analyse how retail experiences are shaped through VAs in a comprative setting of British and Nigerian VA users. The findings enrich the extant literature on user experience of VAs with a granular focus on customer motivations as well as challenges.
Authors
- Mohsin Abdur Rehman (ORCID: https://orcid.org/0000-0002-1044-4487)
- Jyoti Choudrie (ORCID: https://orcid.org/0000-0001-9349-7690)
- Olivia Wright (ORCID: https://orcid.org/0000-0002-6483-6101)
- Ahmad Arslan (ORCID: https://orcid.org/0000-0001-6530-8397)
- Obinna Alo (ORCID: https://orcid.org/0000-0002-2860-6544)
- Paula Danby
Institutions
- University of Hertfordshire (GB)
- Edge Hill University (GB)
- University of Sunderland (GB)
- Oulu University of Applied Sciences (FI)
Publication Details
- Journal
- Qualitative Market Research An International Journal
- Published
- 2025-04-26
- DOI
- https://doi.org/10.1108/qmr-08-2024-0171
- Citations
- 1
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 2.48