This study examines consumers’ intentions to use contact-reducing technologies (CRT) in restaurants and their intentions to purchase from restaurants that use CRT. The study used a theoretical foundation based on the Unified Theory of Adoption and Use of Technology, extended with convenience orientation, media influences, shared goals, and health risks. Data were collected from 985 U.S. consumers who had purchased from a restaurant after March 2020. Social influences have the strongest impact on consumers’ intentions to use CRT in restaurants, and intentions to use CRT and shared goals have significant impacts on consumers’ intentions to purchase from restaurants that use CRT.
AmaroS.DuarteP. (2015). An integrative model of consumers’ intentions to purchase travel online. Tourism Management, 46, 64–79.
2.
BenbasatI.BarkiH. (2007). Quo Vadis, TAM. Journal of the Association of Information Systems, 8(4), 211–218.
3.
BowenJ. T.ChenS. L. (2001). The relationship between customer loyalty and customer satisfaction. International Journal of Contemporary Hospitality Management, 13(5), 213–217.
ChoeJ. Y.KimJ. J.HwangJ. (2021). Perceived risks from drone food delivery services before and after COVID-19. International Journal of Contemporary Hospitality Management, 33(4), 1276–1296.
6.
ChowW. S.ChanL. S. (2008). Social network, social trust and shared goals in organizational knowledge sharing. Information & Management, 45(7), 458–465.
7.
CiftciO.ChoiE.-K.BerezinaK. (2021). Let’s face it: Are customers ready for facial recognition technology at quick-service restaurants?International Journal of Hospitality Management, 95, 102941.
8.
CollierJ. E.SherrellD. L. (2010). Examining the influence of control and convenience in a self-service setting. Journal of the Academy of Marketing Science, 38(4), 490-509.
9.
DeMiccoF.CetronM.DaviesO.GuzmanJ. (2021). COVID-19 impact on the future of hospitality and travel. Journal of Hospitality & Tourism Research, 45(5), 911–914.
10.
FornellC.LarckerD. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
11.
ForoudiP.TabaghdehiS. A. H.MarviR. (2021). The gloom of the COVID-19 shock in the hospitality industry: A study of consumer risk perception and adaptive belief in the dark cloud of a pandemic. International Journal of Hospitality Management, 92, 102717.
12.
GundenN.MorosanC.DeFrancoA. (2020). Consumers’ intentions to use online food delivery systems in the USA. International Journal of Contemporary Hospitality Management, 32(3), 1325–1345.
13.
HaasJ.Davenport SypherB.SypherH. (1992). Do shared goals really make a difference?Management Communication Quarterly, 6(2), 166–179.
14.
HairJ. F.BlackW. C.BabinB. B.AndersonR. E. (2009). Multivariate data analysis (7th ed.). Prenticel Hall.
15.
HeggestadE. D.ScheafD. J.BanksG. C.Monroe HausfeldM.TonihandelS.WilliamsE. B. (2019). Scale adaptation in organizational science research: A review and best-practice recommendations. Journal of Management, 45(6), 2596–2627.
16.
HuH. H. S.HuangC.-T.ChenP.-T. (2010). Do reward programs truly build loyalty for lodging industry?International Journal of Hospitality Management, 29, 128-135
17.
ImJ. Y.HancerM. (2014). Shaping travelers’ attitude toward travel mobile applications. Journal of Hospitality and Tourism Technology, 5(2), 177–193.
18.
JeonH.-M.AliF.LeeS.-W. (2018). Determinants of consumers’ intentions to use smartphones apps for flight ticket bookings. The Service Industries Journal, 39(5–6), 385–402.
19.
KaurP.DhirA.TalwarS.GhumanK. (2021). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33(4), 1129–1159.
20.
KotlerP. T.BowenJ. T.MakensJ.BalogluS. (2016). Marketing for hospitality and tourism (7th ed.). Pearson.
21.
LiM.YinD.QiuH.BaiB. (2021). A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management, 95, 125–135.
22.
López-NicolásC.Molina-CastilloF. J.BouwmanH. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359–364.
23.
LucasA. (2020, September3). Take a look at Burger King’s new “touchless” restaurant designs with solar panes and outdoor seating. CNBC.
24.
MalhotraN. K.KimS. S.PatilA. (2006). Common method variance in is research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.
25.
MorosanC.DeFrancoA. (2016). It’s about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17–29.
26.
MuthènL. K.MuthènB. O. (2017). Mplus user’s guide (8th ed.).
OliverR. L. (1999). Whence consumer loyalty?Journal of Marketing, 63(4), 33–44.
29.
OlsenS. O.MaiH. T. X. (2013). Consumer participation: The case of home meal preparation. Psychology & Marketing, 30(1), 1–11.
30.
Palau-SaumellR.Forgas-CollS.Sánchez-GarcíaJ.RobresE. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 223–231.
31.
PesonenJ.NeidhardtJ. (2020). Special issue: Perspectives on eTourism. Information Technology & Tourism, 22(1), 1–3.
32.
RenaudK.OtondoR.WarkentinM. (2019). “This is the way ‘I’ create my passwords” . . . does the endowment effect deter people from changing the way they create their passwords?Computers & Security, 82, 241–260.
33.
RichterL. K. (2003). International tourism and its global public health consequences. Journal of Travel Research, 30(4), 340–347.
34.
SaarijärviH.KannanP. K.KuuselaH. (2013). Value co-creation: Theoretical approaches and practical implications. European Business Review, 25(1), 6–19.
35.
Salehi-EsfahaniS.KangJ. (2019). Why do you use Yelp? Analysis of factors influencing customers’ website adoption and dining behavior. International Journal of Hospitality Management, 78, 179–188.
36.
SeidersK.VossG. B.GodfreyA. L.GrewalD. (2007). SERVCON: Development and validation of a multidimensional service convenience scale. Journal of the Academy of Marketing Science, 35, 144–156.
37.
ShinH.KangJ. (2020). Reducing perceived health risk to attract hotel customers in the COVID-19 pandemic era: Focused on technology innovation for social distancing and cleanliness. International Journal of Hospitality Management, 91, 102664.
38.
SungY. K.KingB. (2021). Restaurant preventive behaviors and the role of media during a pandemic. International Journal of Hospitality Management, 95, 102906.
VenkateshV.ThongJ. Y.XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
41.
WallaceL.SheetzS. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51(2), 249–259.
42.
WestonR.GoreP. A. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751.
43.
YooC. W.ParameswaranS.KishoreR. (2015). Knowing about your food from farm to the table: Using information systems that reduce information asymmetry and health risks in retail contexts. Information & Management, 52, 692–709.
44.
YuT. Y.YuT. K.ChaoC. M. (2017). Understanding Taiwanese undergraduate students’ pro-environmental behavioral intention towards green products in the fight against climate change. Journal of Cleaner Production, 161, 390–402.
45.
ZemkeD. M.NealJ.ShoemakerS.KirschK. (2015). Hotel cleanliness: Will guests pay for enhanced disinfection?International Journal of Contemporary Hospitality Management, 27(4), 690–710.
