In hospitality and tourism research, cutting-edge technologies (CETs) are receiving growing attention. However, most reviews on CETs focus on specific types of CETs and are devoid of theories focusing on the stakeholders–CETs interaction. Consequently, a comprehensive review and an integrated theoretical framework from an interaction perspective is necessary. To address this gap, we conducted a bibliometric analysis of 554 articles published between 2003 and 2023 to identify three research clusters and key entities of CETs. Moreover, drawing on stimulus-organism-response (SOR) theory and media equation theory, we built a new integrated theoretical framework for understanding the stakeholders’ interaction with the CETs. The focus of this new framework centers on how CETs’ representations and CETs’ types directly and indirectly interact to affect stakeholders’ outcomes. This study represents the first attempt to combine bibliometric and qualitative analysis, contributing to a forward-thinking review and theoretical building that accelerates and enhances research in CETs.
AcedoF. J.BarrosoC.CasanuevaC.GalánJ. L. (2006). Co-authorship in management and organizational studies: An empirical and network analysis. Journal of Management Studies, 43(5), 957–983. https://doi.org/10.1111/j.1467-6486.2006.00625.x
2.
AmeenN.HosanyS.TarhiniA. (2021). Consumer interaction with cutting-edge technologies: Implications for future research. Computers in Human Behavior, 120, Article 106761. https://doi.org/10.1016/j.chb.2021.106761
3.
AngeloniS. (2016). A tourist kit ‘made in Italy’: An ‘intelligent’ system for implementing new generation destination cards. Tourism Management, 52, 187–209. https://doi.org/10.1016/j.tourman.2015.06.011
BogicevicV.SeoS.KandampullyJ. A.LiuS. Q.RuddN. A. (2019). Virtual reality presence as a preamble of tourism experience: The role of mental imagery. Tourism Management, 74, 55–64. https://doi.org/10.1016/j.tourman.2019.02.009
7.
BuhalisD.HarwoodT.BogicevicV.VigliaG.BeldonaS.HofackerC. (2019). Technological disruptions in services: Lessons from tourism and hospitality. Journal of Service Management, 30(4), 484–506. https://doi.org/10.1108/JOSM-12-2018-0398
8.
ChanA. P. H.TungV. W. S. (2019). Examining the effects of robotic service on brand experience: The moderating role of hotel segment. Journal of Travel & Tourism Marketing, 36(4), 458–468. https://doi.org/10.1080/10548408.2019.1568953
9.
ChenC. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
ChoiM.ChoiY.KimS.Badu-BaidenF. (2023). Human vs robot baristas during the COVID-19 pandemic: Effects of masks and vaccines on perceived safety and visit intention. International Journal of Contemporary Hospitality Management, 35(2), 469–491. https://doi.org/10.1108/IJCHM-02-2022-0157
13.
ChristouP.SimillidouA.StylianouM. C. (2020). Tourists’ perceptions regarding the use of anthropomorphic robots in tourism and hospitality. International Journal of Contemporary Hospitality Management, 32(11), 3665–3683. https://doi.org/10.1108/IJCHM-05-2020-0423
ColombettiG. (2005). Appraising valence. Journal of Consciousness Studies, 12(8–9), 103–126.
16.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
17.
FilieriR.LinZ.LiY.LuX.YangX. (2022). Customer emotions in service robot encounters: A hybrid machine-human intelligence approach. Journal of Service Research, 25(4), 614–629. https://doi.org/10.1177/10946705221103937
18.
GardnerW. L.LoweK. B.MossT. W.MahoneyK. T.CogliserC. C. (2010). Scholarly leadership of the study of leadership: A review of The Leadership Quarterly’s second decade, 2000–2009. The Leadership Quarterly, 21(6), 922–958. https://doi.org/10.1016/j.leaqua.2010.10.003
19.
GrundnerL.NeuhoferB. (2021). The bright and dark sides of artificial intelligence: A futures perspective on tourist destination experiences. Journal of Destination Marketing & Management, 19, Article 100511. https://doi.org/10.1016/j.jdmm.2020.100511
HofmanK.WaltersG.HughesK. (2022). The effectiveness of virtual vs real-life marine tourism experiences in encouraging conservation behaviour. Journal of Sustainable Tourism, 30(4), 742–766. https://doi.org/10.1080/09669582.2021.1884690
24.
HuY.MinH.SuN. (2021). How sincere is an apology? Recovery satisfaction in a robot service failure context. Journal of Hospitality & Tourism Research, 45(6), 1022–1043. https://doi.org/10.1177/10963480211011533
IvanovS.SeyitoğluF.MarkovaM. (2020). Hotel managers’ perceptions towards the use of robots: A mixed-methods approach. Information Technology & Tourism, 22(4), 505–535. https://doi.org/10.1007/s40558-020-00187-x
28.
IvanovS. H.WebsterC.StoilovaE.SlobodskoyD. (2022). Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies—A conceptual framework. Tourism Economics, 28(1), 3–26. https://doi.org/10.1177/1354816620946541
29.
JacobyJ. (2002). Stimulus-organism-response reconsidered: An evolutionary step in modeling (consumer) behavior. Journal of Consumer Psychology, 12(1), 51–57. https://doi.org/10.1207/S15327663JCP1201_05
30.
JohnsonR. D.StoneD. L.LukaszewskiK. M. (2020). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 40–52. https://doi.org/10.1108/JTF-02-2020-0013
31.
JordanM. I.MitchellT. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
32.
JungT. H.LeeH.ChungN.tom DieckM. C. (2018). Cross-cultural differences in adopting mobile augmented reality at cultural heritage tourism sites. International Journal of Contemporary Hospitality Management, 30(3), 1621–1645. https://doi.org/10.1108/IJCHM-02-2017-0084
33.
KimH.SoK. K. F.WirtzJ. (2022). Service robots: Applying social exchange theory to better understand human-robot interactions. Tourism Management, 92, Article 104537.https://doi.org/10.1016/j.tourman.2022.104537
34.
KimM. J.LeeC. K.JungT. (2020). Exploring consumer behavior in virtual reality tourism using an extended stimulus-organism-response model. Journal of Travel Research, 59(1), 69–89. https://doi.org/10.1177/0047287518818915
35.
KimS. Y.SchmittB. H.ThalmannN. M. (2019). Eliza in the uncanny valley: Anthropomorphizing consumer robots increases their perceived warmth but decreases liking. Marketing Letters, 30(1), 1–12. https://doi.org/10.1007/s11002-019-09485-9
36.
KimT.LeeH.KimM. Y.KimS.DuhachekA. (2022). AI increases unethical consumer behavior due to reduced anticipatory guilt. Journal of the Academy of Marketing Science. Advance online publication. https://doi.org/10.1007/s11747-021-00832-9
37.
KimW. G.MoonY. J. (2009). Customers’ cognitive, emotional, and actionable response to the servicescape: A test of the moderating effect of the restaurant type. International Journal of Hospitality Management, 28(1), 144–156. https://doi.org/10.1016/j.ijhm.2008.06.010
38.
KnaniM.EchchakouiS.LadhariR. (2022). Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. International Journal of Hospitality Management, 107, Article 103317. https://doi.org/10.1016/j.ijhm.2022.103317
39.
KongH.YuanY.BaruchY.BuN.JiangX.WangK. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717–734. https://doi.org/10.1108/IJCHM-07-2020-0789
40.
LiJ. J.BonnM. A.YeB. H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172–181. https://doi.org/10.1016/j.tourman.2019.02.006
41.
LiX.LiH.PanB.LawR. (2021). Machine learning in internet search query selection for tourism forecasting. Journal of Travel Research, 60(6), 1213–1231. https://doi.org/10.1177/0047287520934871
42.
LinI. Y.MattilaA. S. (2021). The value of service robots from the hotel guest’s perspective: A mixed-method approach. International Journal of Hospitality Management, 94, Article 102876. https://doi.org/10.1016/j.ijhm.2021.102876
43.
LinP. M.PengK. L.RenL.LinC. W. (2019). Hospitality co-creation with mobility-impaired people. International Journal of Hospitality Management, 77, 492–503. https://doi.org/10.1016/j.ijhm.2018.08.013
44.
LiuX. S.YiX. S.WanL. C. (2022). Friendly or competent? The effects of perception of robot appearance and service context on usage intention. Annals of Tourism Research, 92, Article 103324. https://doi.org/10.1016/j.annals.2021.103324
45.
LiuZ.YinY.LiuW.DunfordM. (2015). Visualizing the intellectual structure and evolution of innovation systems research: A bibliometric analysis. Scientometrics, 103(1), 135–158. https://doi.org/10.1007/s11192-014-1517-y
46.
LoureiroS. M. C.GuerreiroJ.AliF. (2020). 20 years of research on virtual reality and augmented reality in tourism context: A text-mining approach. Tourism Management, 77, Article 104028. https://doi.org/10.1016/j.tourman.2019.104028
47.
McCartneyG.McCartneyA. (2020). Rise of the machines: Towards a conceptual service-robot research framework for the hospitality and tourism industry. International Journal of Contemporary Hospitality Management, 32(12), 3835–3851. https://doi.org/10.1108/IJCHM-05-2020-0450
Melián-GonzálezS.Bulchand-GidumalJ. (2020). Employment in tourism: The jaws of the snake in the hotel industry. Tourism Management, 80, Article 104123. https://doi.org/10.1016/j.tourman.2020.104123
50.
MeuterM. L.OstromA. L.RoundtreeR. I.BitnerM. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64. https://www.jstor.org/stable/3203487
51.
MoriM.MacDormanK. F.KagekiN. (2012). The uncanny valley [from the field]. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
52.
MurphyJ.GretzelU.PesonenJ. (2019). Marketing robot services in hospitality and tourism: The role of anthropomorphism. Journal of Travel & Tourism Marketing, 36(7), 784–795. https://doi.org/10.1080/10548408.2019.1571983
53.
NassC.MoonY. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56, 81–103. https://doi.org/10.1111/0022-4537.00153
54.
PillaiR.SivathanuB. (2020). Adoption of artificial intelligence-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(1), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
55.
PrenticeC.Dominique LopesS.WangX. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739–756. https://doi.org/10.1080/19368623.2020.1722304
56.
PrenticeC.WeavenS.WongI. A. (2020). Linking artificial intelligence quality performance and customer engagement: The moderating effect of artificial intelligence preference. International Journal of Hospitality Management, 90, Article 102629. https://doi.org/10.1016/j.ijhm.2020.102629
57.
QiuH.LiM.ShuB.BaiB. (2020). Enhancing hospitality experience with service robots: The mediating role of rapport building. Journal of Hospitality Marketing & Management, 29(3), 247–268. https://doi.org/10.1080/19368623.2019.1645073
58.
RacherlaP.HuC. (2010). A social network perspective of tourism research collaborations. Annals of Tourism Research, 37(4), 1012–1034. https://doi.org/10.1016/j.annals.2010.03.008
59.
RajanK.SaffiottiA. (2017). Towards a science of integrated artificial intelligence and robotics. Artificial Intelligence, 247, 1–9. https://doi.org/10.1016/j.artint.2017.03.003
RibeiroM. A.GursoyD.ChiO. H. (2022). Customer acceptance of autonomous vehicles in travel and tourism. Journal of Travel Research, 61(3), 620–636. https://doi.org/10.1177/0047287521993578
62.
RoeslerE.ManzeyD.OnnaschL. (2021). A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction. Science Robotics, 6(58), Article abj5425. https://doi.org/10.1126/scirobotics.abj5425
63.
SaydamM. B.AriciH. E.KoseogluM. A. (2022). How does the hospitality and tourism industry use artificial intelligence? A review of empirical stud agenda. Journal of Hospitality Marketing & Management, 31(8), 908–936. https://doi.org/10.1080/19368623.2022.2118923
64.
SchwartzZ.UysalM.WebbT.AltinM. (2016). Hotel daily occupancy forecasting with competitive sets: A recursive algorithm. International Journal of Contemporary Hospitality Management, 28(2), 267–285. https://doi.org/10.1108/IJCHM-10-2014-0507
65.
SeyitoğluF.IvanovS. (2020). A conceptual framework of the service delivery system design for hospitality firms in the (post-) viral world: The role of service robots. International Journal of Hospitality Management, 91, Article 102661. https://doi.org/10.1016/j.ijhm.2020.102661
66.
ShiS.GongY.GursoyD. (2021). Antecedents of trust and adoption intention toward artificially intelligent recommendation systems in travel planning: A heuristic–systematic model. Journal of Travel Research, 60(8), 1714–1734. https://doi.org/10.1177/0047287520966395
67.
ShinH. H.ShinS.GimJ. (2022). Looking back three decades of hospitality and tourism technology research: A bibliometric approach. International Journal of Contemporary Hospitality Management, 35(2), 563–588. https://doi.org/10.1108/IJCHM-03-2022-0376
68.
SlevitchL.ChandrasekeraT.SealyM. D. (2022). Comparison of virtual reality visualizations with traditional visualizations in hotel settings. Journal of Hospitality & Tourism Research, 46(1), 212–237. https://doi.org/10.1177/1096348020957067
69.
SolakisK.KatsoniV.MahmoudA. B.GrigoriouN. (2022). Factors affecting value co-creation through artificial intelligence in tourism: A general literature review. Journal of Tourism Futures. Advance online publication. https://doi.org/10.1108/JTF-06-2021-0157
TungV. W. S.LawR. (2017). The potential for hospitality and tourism experience research in human-robot interactions. International Journal of Contemporary Hospitality Management, 29, 2498–2513. https://doi.org/10.1108/IJCHM-09-2016-0520
72.
TuomiA.TussyadiahI. P.StienmetzJ. (2021). Applications and implications of service robots in hospitality. Cornell Hospitality Quarterly, 62(2), 232–247. https://doi.org/10.1177/1938965520923961
73.
TussyadiahI. (2020). A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research, 81, Article 102883. https://doi.org/10.1016/j.annals.2020.102883
74.
TussyadiahI. P.WangD.JungT. H.tom DieckM. C. (2018). Virtual reality, presence, and attitude change: Empirical evidence from tourism. Tourism Management, 66, 140–154. https://doi.org/10.1016/j.tourman.2017.12.003
75.
Van DoornJ.MendeM.NobleS. M.HullandJ.OstromA. L.GrewalD.PetersenJ. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272
76.
Van EckN. J.WaltmanL. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
77.
Van EckN. J.WaltmanL. (2014). Visualizing bibliometric networks. In DingY.WolframD.RousseauR. (Eds.), Measuring scholarly impact: Methods and practice (pp. 285–320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
78.
Van KerrebroeckH.BrengmanM.WillemsK. (2017). Escaping the crowd: An experimental study on the impact of a Virtual Reality experience in a shopping mall. Computers in Human Behavior, 77, 437–450. https://doi.org/10.1016/j.chb.2017.07.019
79.
WaltersG.GillC.PhamL. D. Q.FilepS. (2022). Virtual tourism experiences and mental restoration. Journal of Hospitality & Tourism Research. Advance online publication. https://doi.org/10.1177/10963480221116047
WenJ.KlarinA.GohE.AstonJ. (2020). A systematic review of the sex trafficking-related literature: Lessons for tourism and hospitality research. Journal of Hospitality and Tourism Management, 45, 370–376. https://doi.org/10.1016/j.jhtm.2020.06.001
82.
WirtzJ.PattersonP. G.KunzW. H.GruberT.LuV. N.PaluchS.MartinsA. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119
83.
XuF. Z.ZhangY.ZhangT.WangJ. (2021). Facial recognition check-in services at hotels. Journal of Hospitality Marketing & Management, 30(3), 373–393. https://doi.org/10.1080/19368623.2020.1813670
84.
YangY.LiuY.LvX.AiJ.LiY. (2022). Anthropomorphism and customers’ willingness to use artificial intelligence service agents. Journal of Hospitality Marketing & Management, 31(1), 1–23. https://doi.org/10.1080/19368623.2021.1926037
85.
YeQ.LiT.LawR. (2013). A coauthorship network analysis of tourism and hospitality research collaboration. Journal of Hospitality & Tourism Research, 37(1), 51–76. https://doi.org/10.1177/1096348011425500
86.
YoganathanV.OsburgV. S.KunzW. H.ToporowskiW. (2021). Check-in at the Robo-desk: Effects of automated social presence on social cognition and service implications. Tourism Management, 85, Article 104309. https://doi.org/10.1016/j.tourman.2021.104309
87.
YuC. E. (2020). Humanlike robots as employees in the hotel industry: Thematic content analysis of online reviews. Journal of Hospitality Marketing & Management, 29(1), 22–38. https://doi.org/10.1080/19368623.2019.1592733
88.
YuC. E.NganH. F. B. (2019). The power of head tilts: Gender and cultural differences of perceived human vs human-like robot smile in service. Tourism Review, 74(3), 428–442. https://doi.org/10.1108/TR-07-2018-0097
89.
ZhengW.ZhouR.ZhangZ.ZhongY.WangS.WeiZ.JiH. (2019). Understanding the tourist mobility using GPS: How similar are the tourists?Tourism Management, 71, 54–66. https://doi.org/10.1016/j.tourman.2018.09.019
90.
ZhuD. H.ChangY. P. (2020). Robot with humanoid hands cooks food better? Effect of robotic chef anthropomorphism on food quality prediction. International Journal of Contemporary Hospitality Management, 32(3), 1367–1383. https://doi.org/10.1108/IJCHM-10-2019-0904
91.
ZupicI.ČaterT. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
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