Because of increasing anxiety about infectious diseases and the demand for contactless service caused by the COVID-19 pandemic, it has become crucial for the tourism and hospitality sector to understand customers’ psychological mechanism of contactless service during and post COVID-19. Thus, this paper proposes a conceptual model by integrating trust in the framework of the behavioral immune system. Interestingly, our study found that anxiety about infectious diseases during the COVID-19 pandemic has not only increased hotel customers’ desire for contactless service and changed their behavioral intentions, but it has also impacted customers’ trust in hotel service robots. Therefore, irrespective of how the hotel service environment changes, trust in technology has become the most fundamental factor for hotel customers’ attitudes toward adopting technology. Based on the results, this paper provides salient theoretical and practical implications.
AhujaM. K., & ThatcherJ. B. (2005). Moving beyond intentions and toward the theory of trying: Effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29(3), 427–459.
2.
BaeS. Y., & ChangP. J. (2020). The effect of coronavirus disease-19 (COVID-19) risk perception on behavioural intention towards ‘untact’ tourism in South Korea during the first wave of the pandemic (March 2020). Current Issues in Tourism, 24(7), 1017–1035.
3.
BarlettJ. E.KotrlikJ. W., & HigginsC. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information technology. Learning, and Performance Journal, 19(1), 43–50.
ChoiY.ChoiM.OhM., & KimS. (2020). Service robots in hotels: Understanding the service quality perceptions of human–robot interaction. Journal of Hospitality Marketing and Management, 29(6), 613–635.
6.
CurtisV.AungerR., & RabieT. (2004). Evidence that disgust evolved to protect from risk of disease. Proceedings of the Royal Society of London Series B, 271 (Suppl. 4), S131–S133.
7.
FooL. P.ChinM. Y.TanK. L., & PhuahK. T. (2020). The impact of COVID-19 on tourism industry in Malaysia. Current Issues in Tourism, 24(19), 2735–2739.
8.
FornellC., & LarckerD. F. (1981). Structural equation models with unobservable variables and measurement error. Algebra and Statistics, 18(3), 382–388.
9.
GeisserS. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107.
10.
GösslingS.ScottD., & HallC. M. (2021). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1–20.
11.
GretzelU.FuchsM.BaggioR.HoepkenW.LawR.NeidhardtJ.PesonenJ.ZankerM., & XiangZ. (2020). e-tourism beyond COVID-19: A call for transformative research. Information Technology and Tourism, 22(2), 187–203.
Hair Jr., J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM).Sage Publications: Thousand Oaks (CA).
HoS. H., & KoY. Y. (2008). Effects of self-service technology on customer value and customer readiness: The case of Internet banking. Internet Research, 18(4), 427–446.
16.
InhornM. C., & BrownP. J. (1990). The anthropology of infectious disease. Annual Review of Anthropology, 19(1), 89–117.
17.
JeongM., & LeeS. A. (2017). Do customers care about types of hotel service recovery efforts? Journal of Hospitality and Tourism Technology, 8(1), 5–18.
18.
JiaJ. W.ChungN., & HwangJ. (2021). Assessing the hotel service robot interaction on tourists’ behaviour: The role of anthropomorphism. Industrial Management and Data Systems, 121(6), 1457–1478.
19.
JohnsonD. S.BardhiF., & DunnD. T. (2008). Understanding how technology paradoxes affect customer satisfaction with self-service technology: The role of performance ambiguity and trust in technology. Psychology and Marketing, 25(5), 416–443.
20.
KaushikA. K.AgrawalA. K., & RahmanZ. (2015). Tourist behaviour towards self-service hotel technology adoption: Trust and subjective norm as key antecedents. Tourism Management Perspectives, 16, 278–289.
21.
KhalidU.OkaforL. E., & BurzynskaK. (2021). Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic? Current Issues in Tourism, 24(19), 2801–2820.
22.
KimM. J.ChungN., & LeeC. K. (2011). The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management, 32(2), 256–265.
23.
KimM. J.ChungN.LeeC. K., & KimJ. M. (2012). Do loyalty groups differ in the role of trust in online tourism shopping? A process perspective. Journal of Travel & Tourism Marketing, 29(4), 352–368.
24.
KimN. (2017). Trend Korea 2018. Miraebook Publishing: Seoul.
25.
KimS. Y.KimJ. U., & ParkS. C. (2017). The effects of perceived value, website trust and hotel trust on online hotel booking intention. Sustainability, 9(12), 2262.
26.
KucukustaD.HeungV. C. S., & HuiS. (2014). Deploying self-service technology in luxury hotel brands: Perceptions of business travelers. Journal of Travel and Tourism Marketing, 31(1), 55–70.
27.
LeeC., & WanG. (2010). Including subjective norm and technology trust in the technology acceptance model: A case of e-ticketing in China. ACM SIGMIS Database, 41(4), 40–51.
28.
LeeJ.LeeH., & ChungN. (2020). The impact of customers’ prior online experience on future hotel usage behavior. International Journal of Hospitality Management, 91, 102669.
29.
LeeC. K.SongH. J.BendleL. J.KimM. J., & HanH. (2012). The impact of non-pharmaceutical interventions for 2009 H1N1 influenza on travel intentions: A model of goal-directed behavior. Tourism Management, 33(1), 89–99.
30.
LeeL. Y. S. (2016). Hospitality industry web-based self-service technology adoption model: A cross-cultural perspective. Journal of Hospitality and Tourism Research, 40(2), 162–197.
31.
LiangH., & XueY. L. (2010). Understanding security behaviors in personal computer usage: A threat avoidance perspective. Journal of the Association for Information Systems, 11(7), 1.
LippertS. K., & DavisM. (2006). A conceptual model integrating trust into planned change activities to enhance technology adoption behavior. Journal of Information Science, 32(5), 434–448.
34.
MahmudM. S.TalukderM. U., & RahmanS. M. (2021). Does ‘Fear of COVID-19'trigger future career anxiety? An empirical investigation considering depression from COVID-19 as a mediator. International Journal of Social Psychiatry, 67(1), 35–45.
35.
McKnightD. H.ChoudhuryV., & KacmarC. (2002). The impact of initial consumer trust on intentions to transact with a web site: A trust building model. Journal of Strategic Information Systems, 11(3–4), 297–323.
36.
MorensD. M.FolkersG. K., & FauciA. S. (2004). The challenge of emerging and re-emerging infectious diseases. Nature, 430(6996), 242–249.
37.
NazneenS.HongX., & Ud Din, N. (2020, May 4). COVID-19 crises and tourist travel risk perceptions. SSRN. http://dx.doi.org/10.2139/ssrn.3592321
38.
OhH.JeongM., & BalogluS. (2013). Tourists’ adoption of self-service technologies at resort hotels. Journal of Business Research, 66(6), 692–699.
39.
OhH.JeongM.LeeS., & WarnickR. (2016). Attitudinal and situational determinants of self-service technology use. Journal of Hospitality and Tourism Research, 40(2), 236–265.
40.
ParkS. (2020). Multifaceted trust in tourism service robots. Annals of Tourism Research, 81, 102888.
41.
PappasN. (2017). Effect of marketing activities, benefits, risks, confusion due to over-choice, price, quality and consumer trust on online tourism purchasing. Journal of Marketing Communications, 23(2), 195–218.
42.
PodsakoffP. M.MacKenzieS. B.LeeJ. Y., & PodsakoffN. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
43.
QianTingL.ChungH. C., & ChungN. (2021). A study on the factors affect the technology satisfaction on AI based self-service technology service failure in hotel. In W. Wörndl, C. Koo, & J. L. Stienmetz (Eds.), Information and communication technologies in tourism (pp. 123–127). Springer.
44.
ReinartzW.KrafftM., & HoyerW. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293–305.
45.
SchallerM., & ParkJ. H. (2011). The behavioral immune system (and why it matters). Current Directions in Psychological Science, 20(2), 99–103.
46.
SigalaM. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of business research, 117, 312–321.
47.
SimonF., & UsunierJ. C. (2007). Cognitive, demographic, and situational determinants of service customer preference for personnel-in-contact over self-service technology. International Journal of Research in Marketing, 24(2), 163–173.
48.
StoneM. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B, 36(2), 111– 133.
49.
TsaiJ. Y.PhuaJ.PanS., & YangC. C. (2020). Intergroup contact, COVID-19 news consumption, and the moderating role of digital media trust on prejudice toward Asians in the United States: Cross-sectional study. Journal of Medical Internet Research, 22(9), e22767.
50.
Ul Hassan, M., Iqbal, M. S., & Habibah, U. (2020). Self-service technology service quality: Building loyalty and intention through technology trust in Pakistani service sector. SAGE Open, 10(2), 1–19.
51.
van Leeuwen, F., & Petersen, M. B. (2018). The behavioral immune system is designed to avoid infected individuals, not outgroups. Evolution and Human Behavior, 39(2), 226–234.
52.
WoldH. (1980). Model construction and evaluation when theoretical knowledge is scarce: Theory and application of partial least squares. In KmentaJ. & RamseyJ.B. (Eds.), Evaluation of econometric models (pp. 47–74). Academic Press: New York.
53.
WongK. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1–32.
YenH. R. (2005). An attribute-based model of quality satisfaction for internet self-service technology. Service Industries Journal, 25(5), 641– 659.
56.
YiJ.YuanG., & YooC. (2020). The effect of the perceived risk on the adoption of the sharing economy in the tourism industry: The case of Airbnb. Information Processing & Management, 57(1), 102108.
57.
ZhuH., & DengF. (2020). How to influence rural tourism intention by risk knowledge during COVID-19 Containment in China: Mediating role of risk perception and attitude. International Journal of Environmental Research and Public Health, 17(10), 3514.
