Artificial Intelligence (AI) is transforming service-oriented industries, yet its adoption in developing countries remains underexplored, particularly from the employee perspective. Employees’ perceptions, intentions, and organizational support are pivotal for effective AI integration in hospitality.
Objective
This study investigates the determinants of AI adoption among employees in Egypt’s hotel sector, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT). It examines the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on AI usage, the mediating role of behavioral intention, and the moderating role of gender.
Methods
A quantitative design was applied using a structured online survey administered to 346 frontline employees in five-star hotels in Greater Cairo. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested direct and indirect effects.
Results
Findings reveal that effort expectancy, social influence, and facilitating conditions directly and positively influence AI usage, while performance expectancy affects adoption indirectly via behavioral intention. BI mediates the link between UTAUT constructs and AI use, with gender moderating the intention–usage relationship: male employees exhibit stronger adoption tendencies.
Conclusions
The study underscores the role of user-friendly systems, inclusive training, and gender-sensitive strategies, extending UTAUT by establishing BI as a central mediator and revealing demographic contingencies.
AbassMN. Artificial intelligence techniques in Egyptian hotels: current status and prospective. Egypt J Tourism Hosp2023; 30(1): 1–53.
2.
ChatterjeeSRanaNPDwivediYK. Adoption of artificial intelligence-integrated CRM systems in agile organizations: a UTAUT2 perspective. J Bus Res2021; 124: 1–15.
3.
LimnaP. Artificial intelligence in the hospitality industry: a review article. Int J Comput Sci Res2023; 7: 1306–1317.
4.
AcharyaADattaA. Dimensions of workplace dignity and its influence on intention to quit of hotel employees. J Stat Manag Syst2023; 26(7): 1565–1583.
5.
HouYFanL. Working with AI: the effect of job stress on hotel employees’ engagement and the mediating role of psychological capital. Behav Sci2024; 14(11): 1076.
6.
LawRLinKJYeH, et al.Artificial intelligence research in hospitality: a state-of-the-art review and future directions. Int J Contemp Hospit Manag2024; 36(6): 2049–2068.
7.
SousaMJPaniSDal MasF, et al.Incorporating AI technology in the service sector: innovations in creating knowledge. In: Improving efficiency, and elevating quality of life. CRC Press, 2024.
8.
DoborjehZHemmingtonNDoborjehM, et al.Artificial intelligence: a systematic review of methods and applications in hospitality and tourism. Int J Contemp Hospit Manag2022; 34(3): 1154–1176.
9.
KongHWangKQiuX, et al.Thirty years of artificial intelligence research relating to the hospitality and tourism industry. Int J Contemp Hospit Manag2023; 35(3): 354.
10.
LvHShiSGursoyD. A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism. J Hospit Market Manag2021; 31(2): 145–175.
11.
KumawatEDattaAPrenticeC, et al.Artificial intelligence through the lens of hospitality employees: a systematic review and research agenda. Int J Hospit Manag2025; 124(1): 103986.
12.
GursoyDCaiR. Artificial intelligence: an overview of research trends and future directions. Int J Contemp Hospit Manag2024; 37: 1–17.
13.
IvanovSWebsterC. Adoption of robots, artificial intelligence and service automation by tourism and hospitality companies. Tour Manag Perspect2024; 17: 1–11.
14.
VenkateshVMorrisMGDavisGB, et al.User acceptance of information technology: toward a unified view. MIS Q2003; 27(3): 425–478.
15.
MarghanyMNMElmohandesNMAAMohamadI, et al.Robots at your service: understanding hotel guest acceptance with meta-UTAUT investigation. Int J Hospit Manag2025; 130: 104227.
16.
VenkateshVThongJYXuX. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q2012; 36(1): 157–178.
17.
MihajlovićIStankovićS. The adoption of artificial intelligence in Serbian hospitality. Sustainability2024; 16(8): 3172.
18.
LeeATRamasamyRKSubbaraoA. Understanding psychosocial barriers to healthcare technology adoption: a review of TAM and UTAUT frameworks. Healthcare2025; 13(3): 250.
19.
KimYBlazquezVOhT. Determinants of generative AI system adoption and usage behavior in Korean companies: applying the UTAUT model. Behav Sci2024; 14(11): 1035.
20.
HasaneinAM. Responses to the AI revolution in hospitality and tourism higher education: the perception of students towards accepting and using microsoft copilot. Eur J Investig Health Psychol Educ2025; 15(3): 35.
21.
AboagyeMOMensahEABoatengR. Understanding AI adoption in Ghana’s banking sector through the UTAUT model. Inf Syst Front2023; 25(2): 387–403.
22.
JameelAAlamMKhanS. Technology acceptance of AI-enabled healthcare systems in south Asia: a UTAUT2 perspective. Technol Forecast Soc Change2024; 198: 123867.
23.
OngESTanGWOoiKB. Gender differences in technology acceptance: a study of Mobile commerce in Malaysia. Comput Hum Behav2016; 59: 1–10.
24.
KamalSZA. A meta-analysis of UTAUT-Based empirical evidence. Jurnal Pengurusan2025; 74(2): 1–15.
25.
SadangharnP. Acceptance of robots as co-workers: hotel employees’ perspective. Hospit Soc2022; 6(4): 209–227.
26.
AnditamaMRHidayantoAN. Analysis of factors influencing the adoption of artificial intelligence assistants among software developers. Indones J Comput Sci2024; 13(4): 4425–4454.
27.
XiaYChenY. Driving factors of generative AI adoption in new product development teams from a UTAUT perspective. Int J Hum Comput Interact2024: 1–22.
28.
HasaneinAMSobaihAEE. Drivers and consequences of ChatGPT use in higher education: key stakeholder perspectives. Eur J Investig Health Psychol Educ2023; 13(11): 2599–2614.
29.
AlQeisiKIAl-AbdallahGM. Website design and usage behaviour: an application of the UTAUT model for internet banking in the UK. Int J Market Stud2024; 6: 75–89.
30.
HasaneinAMSobaihAEElshaerIA. Examining google Gemini’s acceptance and usage in higher education. J Appl Learn Teach2024; 7(2): 223.
31.
AldogiherAHalimYTEl-DeebMS, et al.The impact of digital teaching technologies in Saudi and Egyptian universities on institutional sustainability: the mediating role of change management and the moderating role of culture, technology, and economics. Sustainability2025; 17(5): 2062.
32.
EssienAChukwukeluG. Deep learning in hospitality and tourism: a research framework agenda for future research. Int J Contemp Hospit Manag2023; 34: 4480–4515.
33.
ÇalıskanGYaylaIPamukçuH. The use of augmented reality technologies in tourism businesses from the perspective of UTAUT2. Eur J Innovat Manag. 2025; 28(4): 1498–1526.
34.
SaidGRE. Factors affecting mHealth technology adoption in developing countries: the case of Egypt. Computers2023; 12(1): 9.
35.
Díaz-ArancibiaJHochstetter-DiezJBustamante-MoraA, et al.Navigating digital transformation and technology adoption: a literature review from small and medium-sized enterprises in developing countries. Sustainability2024; 16(14): 5946.
36.
ChangPCZhangWCaiQ, et al.Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychol Res Behav Manag2024; 31: 413–427.
37.
MalikANguyenTMBudhwarP. Towards a conceptual model of AI-mediated knowledge sharing exchange of HRM practices: antecedents and consequences. IEEE Trans Eng Manag2022; 71: 13083–13095.
38.
AlalwanAAAl-DebeiMMAl-LoziE. Gender differences in the adoption of artificial intelligence in the hospitality industry: a UTAUT2 perspective. Int J Hospit Manag2022; 102: 103105.
39.
YinMHanBRyuS, et al.Acceptance of generative AI in the creative industry: examining the role of AI anxiety in the UTAUT2 model. Int Conf Hum Comput Interact. Cham: Springer Nature Switzerland, 2023, pp. 288–310.
40.
GursoyDChiCG. Artificial intelligence in hospitality: implications for employees and organizations. Int J Hospit Manag2023; 98: 103050.
41.
CaseyTWilson-EveredE. Predicting uptake of technology innovations in online family dispute resolution services: an application and extension of the UTAUT. Comput Hum Behav2012; 28(6): 2034–2045.
42.
KijsanayotinBPannarunothaiSSpeedieSM. Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model. Int J Med Inf2009; 78(6): 404–416.
43.
VenkateshV. Adoption and use of AI tools: a research agenda grounded in UTAUT. Ann Oper Res2022; 308(1): 641–652.
44.
ShahsavarYChoudhuryA. User intentions to use ChatGPT for self-diagnosis and health-related purposes: cross-Sectional survey study. JMIR Hum Factors2023; 10(1): e47564.
45.
KhanMSKhanMA. Determining behavioral intention to use artificial intelligence in the hospitality sector of Pakistan: an application of the UTAUT model. J Tourism Hosp Serv Ind Res2024; 4(1): 67–85.
46.
Ul HassanMNazA. Education for women entrepreneurial attitudes and intentions: the role of perceptions on gender equality and empowerment. Pak J Commer Soc Sci2020; 14(1): 63–98.
47.
PresserSCouperMPLesslerJT, et al.Methods for testing and evaluating survey questions. Methods for testing and evaluating survey questionnaires, 2004, pp. 1–22.
48.
DillmanDASmythJDChristianLM. Internet, phone, mail, and mixed-mode surveys: The tailored design method. 2014, vol 17.
49.
EtikanIMusaSAAlkassimRS. Comparison of convenience sampling and purposive sampling. Am J Theor Appl Stat2016; 5(1): 1–4.
50.
TaherdoostH. Sampling methods in research methodology; how to choose a sampling technique for research. Int J Acad Res Manag. 2016; 5: 18–27.
51.
GoodmanLA. Statistical methods for the mover-stayer model. J Am Stat Assoc1961; 56(296): 841–868.
52.
HeckathornDD. Comment: snowball versus respondent-driven sampling. Sociol Methodol2011; 41(1): 355–366.
53.
HairJMatthewsLMMatthewsRL, et al.PLS-SEM or CB-SEM: updated guidelines on which method to use. Int J Multivar Data Anal2017; 1(2): 107.
54.
PodsakoffPMMacKenzieSBLeeJY, et al.Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol2003; 88(5): 879–903.
55.
HenselerJRingleCMSinkovicsRR. The use of partial least squares path modeling in international marketing. In: New challenges to international marketing. Emerald Group Publishing Limited, 2009, pp. 277–319.
56.
MaxwellSEColeDA. Bias in cross-sectional analyses of longitudinal mediation. Psychol Methods2007; 12(1): 23–44.
57.
Do VallePOAssakerG. Using partial least squares structural equation modeling in tourism research: a review of past research and recommendations for future applications. J Trav Res2016; 55(6): 695–708.
58.
KockN. Common method bias in PLS-SEM: a full collinearity assessment approach. Int J e-Collaboration2015; 11(4): 1.
59.
ChinW. Commentary: issues and opinion on structural equation modeling. J Assoc Inf Syst Online1998; 22: 249674, Available from: https://www.jstor.org/stable/249674
60.
CohenJ. Set correlation and contingency tables. Appl Psychol Meas1988; 12(4): 425–434.
