ENTER2020 eTourism Conference presentation

1. Customer Intention to Use Facial Recognition at
Quick-Service Restaurants
Olena Ciftci, Eun-Kyong Choi, Ph.D., and Katerina Berezina, Ph.D.
Page 11/20/2020

2. Introduction
Facial recognition system in BurgerFi
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3. Introduction
• Facial recognition is technology that
can identify individuals by their face
topology (Maxie, 2017; Unar, Seng, &
Abbasi, 2014).
• Facial recognition is a type of
biometrics technology
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4. Introduction
• Potential to use for loyalty program
account access and/or payment
authorization
• chains Chick-Fil-A, KPro by KFC in China,
BurgerFi;
• start-ups CaliBurger in California and
Malibu Poke in Dallas, Texas.
• Integrated with other technology systems
in restaurants
• self-service kiosks,
• loyalty program system
• point-of-sales systems (POS)
Facial recognition system in CaliBurger
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5. Introduction
Benefits from deploying facial recognition in quick-service restaurants:
• convenience for customers
• much faster process of ordering
• personalized service
• enhancement of the customer experience
• differentiation from competitors
• attraction of new customers (Kim, Tang, & Bosselman, 2018).
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6. Introduction
Industry problem
Promising massive adoption business investment in technology
(Morosan, 2011).
Lack of academic literature on the topic
Morosan, C. (2011). Customers' adoption of biometric systems in restaurants: An extension
of the technology acceptance model.
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7. Introduction
Main goal
• To investigate the factors influencing customers’ adoption of facial recognition in
quick-service restaurants.
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8. Theoretical model
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H1
H2
H3
H4
H5
H6
Intention to Use for a
Loyalty Account
Authorization
Intention to Use for a
Payment
Authorization
Behavioral Intentions
a)
b)
UTAUT
UTAUT2
Context
Performance
Expectancy
Effort Expectancy
Social Influence
Facilitating
Conditions
Hedonic Motivations
Personal
Innovativeness
Privacy Concerns
Perceived Security
Trust
H8
H9
H7
Unified Theory of
Acceptance and Use of
Technology:
UTAUT (Venkatesh, Morris,
Davis, & Davis, 2003) and
UTAUT 2 (Venkatesh, Thong,
& Xu, 2012).

9. Methods
Data collection
• A self-administrated online questionnaire was created in Qualtrics
• Distributed though Amazon Mechanical Turk (MTurk)
• Pilot test
• Sample size
• 558 useful responses
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10. Methods
Measures
• Multi-items constructs
• A seven-point Likert scale with anchors from “1-Strongly Disagree” to “7- Strongly
Agree”
• The items were derived from previous literature and changed when necessary to fit the
study context (Agarwal & Prasad, 1998; Breward et al., 2017; Kim et al., 2011;
Khalilzadeh et al., 2017; Morosan, 2011; Okumus et al., 2018; Pavlou et al., 2007;
Vekantesh et al., 2012)
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11. Methods
Questionnaire structure
• Screening questions
• The U.S. residents
• at least 18 years old or older
• have dined in a QSR within the last 12 months
• ordered a meal or drink through a self-service kiosk at least once.
• Scenario with photos of the system
• Questions: items of the constructs
• Questions about participants’ dining behavior
• Demographic questions
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12. Analysis
• Descriptive statistics
• SPSS 24
• Confirmatory factor analysis (CFA) to evaluate the measurement model
• Partial least squares (PLS) based SEM to examine the structural model and hypotheses
(Hair, Hult, Ringle, & Sarstedt, 2013)
• Smart PLS 3.6
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13. Findings
• 49.7% males and 49.5% females
• 67.9 % 25 - 44 years old
• 45.4% single and 45.5 % married
• More than 85% were college graduates
• Majority have dined in QSRs within a month
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Sample demographic and dining statistics

14. Findings Convergent validity of the measurement model
Constructs Loadings AVE CR
Effort Expectancy (EE) > 0.8 0.826 0.950
Facilitating Conditions (FC) >0.6 0.617 0.826
Hedonic Motivations (HM) >0.9 0.927 0.974
Performance Expectancy (PE) >0.8 0.847 0.957
Personal Innovativeness (PI) >0.7 0.583 0.807
Privacy Concerns (PC) >0.9 0.877 0.973
Social Influence (SI) >0.9 0.910 0.968
Perceived Security (PS) >0.9 0.952 0.975
Trust (T) >0.9 0.884 0.958
Intention to Use for a Loyalty Account
Authorization (IULA)
>0.9 0.968 0.989
Intention to Use for a Loyalty Payment
Authorization (IUPA)
>0.9 0.967 0.989
Table 1. Validity and reliability for constructs.
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15. Findings Discriminant validity of the measurement model
Table 2. Heterotrait-Monotrait Ratios (HTMTs).
(Henseler, Ringle, & Sarstedt, 2015)
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EE FC HM IULA IUPA PC PS PE PI SI T
EE
FC 0.754
HM 0.422 0.481
IULA 0.300 0.415 0.757
IUPA 0.267 0.379 0.716
PC 0.099 0.211 0.440 0.592 0.593
PS 0.240 0.377 0.692 0.859 0.880 0.681
PE 0.354 0.412 0.785 0.865 0.828 0.499 0.782
PI 0.388 0.363 0.260 0.306 0.277 0.101 0.228 0.280
SI 0.232 0.349 0.679 0.803 0.790 0.472 0.762 0.801 0.257
T 0.345 0.495 0.791 0.836 0.822 0.602 0.885 0.825 0.239 0.796

16. Findings Structural Model
Table 3. Hypotheses testing and effect size.
Note: * The effect size was determined based on Cohen’s (1988) guidelines.
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Hypotheses Beta t-value p-value f Square*
H1a PE  IULA 0.382 7.562 0.000 0.191 (medium effect)
H1b PE  IUPA 0.333 6.205 0.000 0.120 (small effect)
H2a EE  IULA -0.042 1.672 0.095 0.005 (no effect)
H2b EE  IUPA -0.051 2.135 0.033 0.006 (no effect)
H3a SI  IULA 0.210 4.887 0.000 0.071 (small effect)
H3b SI  IUPA 0.232 4.912 0.000 0.071 (small effect)
H4a FC  IULA 0.016 0.586 0.558 0.001 (no effect)
H4b FC  IUPA 0.003 0.104 0.917 0.000 (no effect)

17. Findings Structural Model
Table 3. Hypotheses testing and effect size (Continue)
Note: * The effect size was determined based on Cohen’s (1988) guidelines.
R2=0.782 and R2=0.736 for intention to use facial recognition for loyalty account authorization and payment respectively
R2=0.700 for trust towards facial recognition
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Hypotheses Beta t-value p-value f Square*
H5a HM  IULA 0.126 3.171 0.002 0.026 (small effect)
H5b HM  IUPA 0.077 1.953 0.051 0.008 (no effect)
H6a PI  IULA 0.054 2.321 0.020 0.012 (no effect)
H6b PI  IUPA 0.044 1.832 0.068 0.007 (no effect)
H7a T  IULA 0.251 5.796 0.000 0.081 (small effect)
H7b T  IUPA 0.307 6.808 0.000 0.100 (small effect)
H8 PC  T -0.051 1.793 0.073 0.005 (no effect)
H9 PS  T 0.803 28.962 0.000 1.234 (large effect)

18. Conclusions
• Theoretical contribution
• Extending UTAUT for the context of facial recognition in QSRs
• Comparing intentions to use facial recognition for two types of authorization: to a loyalty account
and a payment account.
• Practical implication of the results for the quick-service restaurants management.
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19. Limitations
• A non-probability sampling
• The sample of people who have dined in QSRs in last 12 months.
• The scenario is about facial recognition integrated in self-service kiosk
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20. Thank you very much for your
attention!
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21. References
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• Maxie, E. (2017, September 5). Ready or not, facial recognition is here to stay. Retrieved from https://www.verypossible.com/blog/ready-or-not-facial-
recognition-is-here-to-stay
• Morosan, C. (2011). Customers' adoption of biometric systems in restaurants: An extension of the technology acceptance model. Journal of Hospitality Marketing &
Management, 20(6), 661-690.
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voice recognition and middleware recognition) and end user (public sector, banking & financial sector, healthcare, IT & telecommunication and others) - Global
opportunity analysis and industry forecast, 2015 – 2022. Reports overview. Retrieved from https://www.alliedmarketresearch.com/biometric-technology-market
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doi:10.1016/j.patcog.2014.01.016
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