Examining consumer intentions to use smart retail technology
Smart retail technologies (SRT), such as nearfield communication, interactive displays, touch screens, and self-checkout systems have seen widespread penetration to improve operational efficiencies and signal retailer innovation to consumers.
However, the factors determining consumer acceptance of these technologies have been inconstantly identified in the literature, making it difficult to predict which consumers will respond positively to which SRTs. There exist both customer level factors, such as consumer’s confidence and ability to use these technologies, and technology level factors such as the novelty of the experience comparative to competition. However, without a unified approach to understanding these factors it is difficult to understand how consumers obtain value from their interactions with SRT and how to best support positive experiences and behavioural outcomes.
This research draws upon the innovation adoption theory and information processing of product attributes, arguing that past research has focused too much on individual attributes, and that adoption is the result of a relationship between consumer attributes and inter-linked context and SRT attributes. Specifically, it proposes a conceptual model through which perceived novelty (newness and innovation), perceived efficacy (confidence in using SRT), perceived compatibility (alignment with expectations and values), and perceived risk (uncertainty regarding SRT outcomes) of SRT determine consumers’ intentions to use SRT, which, in turn, influences their perception of value for money and engagement with the shopping experience.
Method and sample
A self-completion online survey was administered to a North American consumer panel (N=338) recruited via mTurk. This survey was scenario-based, capturing options from customers after they interacted with a simulated smart retail cart SRT. Each construct in the model was measured via previously validated scales, which were adapted to suit the context. Data from this survey was then used to test the model through structural equation modelling (SEM) to establish the validity of the model and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to explore how different factors interact with each other to influence outcomes.
Key findings
1. Intentions to use SRT were predicted by how shoppers perceived the technology’s newness and innovation, its alignment with their needs and expectations, and whether they were confident they could successfully and easily use the technology.
2. However, whether shoppers felt that the SRT would make their shopping ‘easier’ did not alone predict their intention to use. This may be because shoppers on mTurk were already reasonably confident in using SRT easily, or ‘ease of shopping’ was only important in conjunction with other factors.
3. Several types of shoppers emerged from this research. Two of these predicted use intention: both needed the SRT to align with how they already wanted to shop, however ‘technologically-savvy’ shoppers also wanted novelty and innovation, while ‘convenience-seeking’ shoppers needed to feel that the SRT would make their shopping easier. This shows that while this ‘efficacy’ factor isn’t important across the whole population, it is important to specific user groups.
4. Shoppers who were already inclined to trial the SRT were identified as ‘curious’. When these shoppers perceived newness and innovation in the SRT they experienced deeper engagement in the shopping process.
5. SRT is also capable of increasing shopper’s perception of value for money in some consumer groups; ‘Market-explorers’, experienced value when the SRT delivered novelty, efficacy and compatibility, while ‘pro-experience’ shoppers attained value when they felt confident in using the SRT effectively.
Recommendations
This research shows that consumer adoption and interaction with smart technology is not just the result of maximising how shoppers perceive the SRT’s attributes: its innovativeness, its ability to make the shopping experience easier, compatibility with shopper expectations or their confidence in their ability to use the SRT, but rather in how different shopper groups react to bundles of these attributes.
In particular, this research suggests that intention to use SRT’s is the result of alignment between how compatible the technology with how customers’ currently shop (compatibility) and either its newness and innovation (novelty) in the case of technologically-savvy shoppers or its ability to make shopping easier (efficacy) in the case of convenience-seeking shoppers.
This suggests that retail managers should focus on different design goals for their smart technologies based on their understanding of their shopper’s needs. However, given that compatibility is a core condition for both shopper groups, managers seeking to increase acceptance of SRTs should first design for compatibility with their customer’s retail decision-making process. For example, retailers can use digital assistants to provide retail shoppers with updated information on products and opportunities to compare products with alternative offerings.
How engaged shoppers were with the use of SRT’s, a key component of positive interaction and a predictor of future use, was again dependent on a combination of different attributes for different shoppers. However, a shopper’s perceived ability to successfully use the SRT (its risk factor) was a key factor across several groups. This suggests that mangers should both design for ease of use but also provide employee support and customer training to increase positive experiences with SRTs and encourage their long-term adoption.
This research highlights that there is no ‘one size fits all’ approach to smart retail technology, and that managers should build their SRT’s from a foundational understanding of their customer’s needs. However, there exist some common factors, namely compatibility and risk, that can be used as design goals across multiple retail contexts.
Researcher
More information
The research article is also available on eprints.