Let’s face it: Are customers ready for facial recognition technology at quick-service restaurants?
Introduction
The biometric technology market is growing at tremendous speed (Sonawane, 2016). Biometric systems identify individuals by measuring and analyzing their unique physical (e.g., face, fingerprint, and iris) and behavioral (e.g., voice and gate) characteristics (Unar et al., 2014, Sonawane, 2016, Rouse, 2017). Due to its technological advancements, including increased accuracy in identifying people by their unique features, convenience, and ease of use, biometric systems have been employed by not only governmental agencies but also private companies, such as banks, airports, retail, and e-commerce (Sonawane, 2016).
Biometric technologies are also finding their way into the restaurant industry because incorporating such innovative technologies may help restaurants enhance customer experience, thus, improving their abilities to differentiate their businesses from competitors and attract new customers (Kim et al., 2018). While biometric technologies can be implemented at restaurants in diverse ways, one of the widely used applications that enhances company-customer interaction is customer identification and authorization for loyalty program accounts and/or payment accounts (Morosan, 2011). For example, customers could be identified by scanning their faces through a kiosk, which could then locate them in a loyalty program module or point-of-sale systems (POS), which would allow customers to access their restaurant account, get personalized suggestions, and pay for their orders in seconds using only their faces; they would not need to type phone numbers, enter passwords, remember PIN codes, or swipe credit cards (Unar et al., 2014, Wu, 2017, Hamstra, 2018). The process of identifying a person based on their face topography is done by the type of biometric technology that is called facial recognition (Unar et al., 2014, Maxie, 2017) and usually works in the system with artificial intelligence and other systems (e.g., check-in mobile applications, self-service kiosks, and security cameras).
Among the different restaurant segments, quick-service restaurants (QSRs) have been at the forefront of adopting facial recognition systems (FRSs) into their businesses due to having less emphasis on interactions between employees and customers and the importance of speedy service. Chick-Fil-A (Maxie, 2017), KPro by KFC in China (Hawkins, 2017), BurgerFi (Hamstra, 2018), and start-ups CaliBurger in California (Wu, 2017) and Malibu Poke in Dallas, Texas (Rankin, 2017), are examples that have already tested or implemented FRSs for their loyalty program account access and/or payment account authorization. With these technologies, restaurants can even provide contactless services to their customers (Hamilton, 2020).
Despite the increasing popularity and potential benefits that biometric technologies bring to a restaurant, surprisingly, customer willingness to adopt FRS when dining out has not been fully addressed in the existing academic literature. To the best of the authors’ knowledge, the only available study related to restaurant customer intention to use biometric technology was conducted by Morosan (2011), who focused on the adoption of biometric technologies in the restaurant industry in general. While providing foundational knowledge in the area of customer adoption of biometric technology in the restaurant industry, that study failed to consider that various biometric systems have different characteristics (Jackson, 2009, Sonawane, 2016). In addition, the previous literature (e.g., Murphy and Rottet, 2009) suggested that the customer willingness to use a certain biometric technology might differ by the purpose and the situation, but there is no study pertaining to the customer likelihood to use different technologies for different purposes in the restaurant setting.
Given the literature described above, the purpose of this research is to develop and test an integrated model that examines the determinants of customer intention to use facial recognition, one of the most frequently used biometric technologies in QSRs. Specifically, this study examines the QSR customer adoption intentions from two perspectives, namely adoption for (1) loyalty program accounts and (2) payment account authorization by extending the unified theory of acceptance and use of technology (UTAUT) proposed by Venkatesh et al. (2003) with additional factors (e.g., hedonic motivations, customer personal innovativeness, privacy concerns, perceived security, and trust in the system) that previous researchers have identified as direct or indirect influences on customer intention to use biometric technologies.
Taken all together, the significance of this study for academic and professional audiences is two-fold. First, the study extends the UTAUT and introduces a novel integrated model of factors influencing consumer intention to adopt FRS technology in QSR that is designed and contextualized for the specific restaurant segment. Second, this research differentiates between consumer intention to adopt FRS technology for two purposes, such as their loyalty account and their payment account authorization.
Section snippets
Biometric technology
Biometric technology is used mainly for the identification and authentication of an individual by measuring and analyzing their physical and behavioral features (Unar et al., 2014, Sonawane, 2016, Rouse, 2017). Biometric authentication refers to accurate identity verification of every person based on their inherent physical or behavioral characteristics (Unar et al., 2014, Rouse, 2017). While biometric technologies based on physical biometric traits include recognition of an individual by their
Survey design
This study population consisted of U.S. residents who are at least 18 years or older, have dined in a QSR within the last 12 months, and ordered a meal or drink through a self-service kiosk at least once. Therefore, the questionnaire contained screening questions about the participants' age, QSR dining experience, and the use of a self-service kiosk. After the screening questions, participants read the definition of FRS and a scenario where they were asked to imagine that they were ordering
The sample demographics and dining experience
The sample was almost equally represented by males (49.8%) and females (49.5%) as well as single (45.4%) and married (45.5%) respondents. The majority of the respondents were between 25 and 44 years old (67.9%) and working 40 h or more (59%) (See the full sample demographic profile in Table 1). Table 2 contains information about the sample dining experience.
Descriptive statistics
According to descriptive statistics (see Table 3), the respondents reported a medium level of intention to use FRS in QSRs for both loyalty
Discussion and practical implications of the findings
This study identified a set of antecedents for predicting customer intention to use FRS in QSRs for loyalty and payment account authorization. The results of the model testing demonstrated high predictive (Q2 > 0.59) and explanatory power (R2 > 0.700) for all dependent variables. The study’s findings revealed the factors that significantly and positively affect customer intention to use FRS for both loyalty and payment accounts. Such factors include perceived performance expectancy, social
Declarations of interest
None.
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