Abstract
Purpose: This paper aims to categorize users of voice assistants and analyze decision-making conflicts to predict intention to adopt voice commerce (v-commerce).
Design/methodology/approach
This exploratory study used expert survey-based data collection founded on data saturation.
Findings: This study identifies three forms of voice systems based on senses aroused (screen first, voice only and voice first) and four profiles of voice users (passive resistant, hedonistic adopter, utilitarian adopter and active resistant), each with a different appraisal of the benefits and costs of v-commerce adoption and the experiences (positive or negative) felt during the shopping experience. This study proposes a conceptual model to predict intention to adopt v-commerce depending on voice-system and -user characteristics.
Practical implications: Learning from this study can help improve the marketing strategies and actions put in place by voice-assistant brands and advertisers by providing insights for adapting product recommendation algorithms to meet the needs of the identified profiles.
Originality/value: This paper provides an answer to the limits of classical approaches based on “one-size-fits-all” strategy by showing how voice-assistant users have different profiles that span a gradient of advance in technology adoption.
Design/methodology/approach
This exploratory study used expert survey-based data collection founded on data saturation.
Findings: This study identifies three forms of voice systems based on senses aroused (screen first, voice only and voice first) and four profiles of voice users (passive resistant, hedonistic adopter, utilitarian adopter and active resistant), each with a different appraisal of the benefits and costs of v-commerce adoption and the experiences (positive or negative) felt during the shopping experience. This study proposes a conceptual model to predict intention to adopt v-commerce depending on voice-system and -user characteristics.
Practical implications: Learning from this study can help improve the marketing strategies and actions put in place by voice-assistant brands and advertisers by providing insights for adapting product recommendation algorithms to meet the needs of the identified profiles.
Originality/value: This paper provides an answer to the limits of classical approaches based on “one-size-fits-all” strategy by showing how voice-assistant users have different profiles that span a gradient of advance in technology adoption.
| Original language | English |
|---|---|
| Pages (from-to) | 800-813 |
| Number of pages | 14 |
| Journal | Journal of consumer marketing |
| Volume | 39 |
| Issue number | 7 |
| Early online date | 5 Oct 2022 |
| DOIs | |
| Publication status | Published - 17 Nov 2022 |
Keywords
- 2023 OA procedure