INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
Technology Implementation and Guests’ Visit Intentions: A  
Generational Analysis between Millennials and Generation Z in  
Malaysian Hotels  
Afdhal Afiq Lasila, Hanis Amanina Mohamad Hairi, Nadia Hanin Nazlan  
Faculty of Hotel & Tourism Management, Universiti Teknologi MARA Cawangan Selangor  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000509  
Received: 20 October 2025; Accepted: 28 October 2025; Published: 17 November 2025  
ABSTRACT  
The hospitality industry is rapidly transforming through digital innovation, with hotels increasingly adopting  
technologies such as smart-room systems, self-check-in kiosks, and mobile key access to enhance service quality  
and guest satisfaction. This study investigates the influence of technology implementation on guests’ visit  
intentions, focusing on generational differences between Millennials and Generation Z, two cohorts that  
dominate the contemporary travel market. Drawing upon the Unified Theory of Acceptance and Use of  
Technology (UTAUT), four constructs; performance expectancy, effort expectancy, social influence, and  
facilitating conditions were examined to determine their effects on visit intention. A quantitative, cross-sectional  
design was employed using an online survey of 347 hotel guests in Malaysia (162 Millennials, 185 Generation  
Z). Partial Least Squares Multi-Group Analysis (PLS-MGA) was used to assess measurement validity and test  
hypotheses. Results indicate that effort expectancy significantly predicts visit intention for both generations,  
while facilitating conditions exert a significant effect only among Millennials. Performance expectancy and  
social influence were found to be non-significant across groups. These findings highlight the central role of ease  
of use and infrastructural support in technology adoption within hospitality services, as well as the differing  
expectations of each generation. The study extends the UTAUT model to a multigenerational hospitality context  
and offers actionable insights for hotel operators to design inclusive, user-friendly, and technologically enhanced  
experiences that align with the post-digital era of hospitality.  
Keywords: UTAUT, hospitality technology, generational difference, visit intention, Malaysia  
INTRODUCTION  
The hospitality industry has entered an era of digital transformation in which technology has become integral to  
service delivery and guest satisfaction. From mobile check-in systems and smart-room controls to artificial  
intelligence (AI)-driven concierge applications, digital innovation is redefining how hotels operate and how  
guests interact with services (Buhalis et al., 2024; Wu et al., 2024). These technologies not only increase  
operational efficiency but also facilitate personalized, seamless, and contactless experiences that align with  
changing consumer expectations. In Malaysia, technological innovation has accelerated post-pandemic as hotels  
adopt digital solutions to enhance competitiveness, ensure safety, and improve service convenience. Despite  
these advances, a persistent question remains: do different generations of guests perceive and respond to hotel  
technologies in the same way?  
Understanding how technology influences guests’ behavioral intentions is critical in a service landscape that  
increasingly blends physical and digital touchpoints. Behavioral intention, conceptualized as a person’s  
willingness to engage with a particular service or brand, serves as a proxy for actual behavior such as revisiting  
or recommending a hotel (Pan et al., 2022). The Unified Theory ofAcceptance and Use of Technology (UTAUT)  
developed by Venkatesh et al. (2003) provides a comprehensive model for understanding technology adoption  
behaviors. The model posits that four constructs; performance expectancy, effort expectancy, social influence,  
and facilitating conditions, jointly determine behavioral intention and use behavior. While widely applied in  
information systems research, the UTAUT framework has gained traction in hospitality contexts for explaining  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
both employee and guest adoption of digital tools (Ibrahim & Islam, 2024; Hao, 2021). However, its application  
to multigenerational consumer groups remains limited, particularly within the Southeast Asian hospitality sector.  
Millennials (born 1981–1996) and Generation Z (born 1997–2012) together constitute the most dominant  
demographic groups in today’s travel market. Both generations are digital natives but differ significantly in their  
technological motivations and expectations. Millennials witnessed the evolution of digitalization and often view  
technology as a tool for convenience, time efficiency, and enhanced productivity (Calvo-Porral & Pesqueira-  
Sanchez, 2019). They value intuitive systems but also appreciate structured support and reliability. In contrast,  
Generation Z was born into a hyperconnected, mobile-driven world and perceives technology not merely as a  
tool but as an extension of lifestyle and identity (Dolot, 2018). They expect immediacy, automation, and  
personalization, often preferring minimal human interaction if digital systems meet their standards (Seyfi et al.,  
2024). These generational nuances suggest that the same hotel technology may evoke different cognitive and  
emotional responses, leading to variations in visit intentions.  
Research in hospitality technology has largely focused on general adoption trends or on single generations, often  
neglecting intergenerational comparisons. Studies by Romero and Lado (2021) and Chen et al. (2022) examined  
Generation Z’s acceptance of service robots, while others such as Rauf et al. (2022) explored Millennials’  
attitudes toward AI-driven service interfaces. However, few investigations have compared both cohorts  
simultaneously to assess whether the determinants of technology-driven visit intentions differ between them.  
Moreover, contextual factors in developing markets like Malaysia, where technological infrastructure and  
consumer readiness vary; further highlighting the need for comparative inquiry. Addressing this research gap  
will contribute to both theoretical refinement and managerial insight in the domain of hospitality technology  
adoption.  
Malaysia offers a relevant empirical setting for such an investigation. As a fast-developing tourism destination,  
the country has positioned digital transformation as a strategic priority under its national tourism framework.  
The Ministry of Tourism, Arts and Culture (2023) emphasizes smart tourism and service automation as critical  
pathways for enhancing destination competitiveness. Yet, within Malaysian hotels, technology adoption rates  
and guest acceptance levels remain uneven. Younger travelers, particularly Millennials and Generation Z, are  
often the first to embrace digital interactions, while older guests continue to prefer traditional face-to-face  
engagement. Understanding how generational differences shape attitudes toward hotel technologies can help  
industry practitioners craft inclusive strategies that align innovation with guest diversity.  
Grounded in the UTAUT framework, this study investigates how technology implementation in hotels influences  
guests’ visit intentions, while explicitly accounting for generational differences between Millennials and  
Generation Z. The study’s overarching objective is to evaluate whether the key determinants of technology  
adoption—performance expectancy, effort expectancy, social influence, and facilitating conditions—vary in  
their impact across these generational cohorts. By doing so, this research bridges theoretical and empirical gaps  
in hospitality technology adoption studies and advances a generationally sensitive interpretation of digital  
behavior.  
Accordingly, the following hypotheses are proposed:  
H1: The effect of performance expectancy on guests’ visit intentions toward technology-enabled hotels differs  
significantly between Millennials and Generation Z.  
H2: The effect of effort expectancy on guests’ visit intentions toward technology-enabled hotels differs  
significantly between Millennials and Generation Z.  
H3: The effect of social influence on guests’ visit intentions toward technology-enabled hotels differs  
significantly between Millennials and Generation Z.  
H4: The effect of facilitating conditions on guests’ visit intentions toward technology-enabled hotels differs  
significantly between Millennials and Generation Z.  
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By integrating generational perspective directly into the UTAUT model, this study not only enhances  
understanding of behavioral intention in technology-mediated hospitality environments but also provides  
actionable insights for hotel operators seeking to design user-centric, inclusive digital experiences. The findings  
are expected to contribute to both theoretical enrichment and managerial decision-making, aligning with  
Malaysia’s broader vision for sustainable and innovation-driven tourism in the digital era.  
LITERATURE REVIEW  
Evolution of Technology in the Hotel Industry  
Technology has been integral to the hospitality industry’s modernization for more than six decades. Early hotel  
systems in the 1950s and 1960s primarily supported back-office operations such as reservations, billing, and  
accounting. The introduction of electronic reservation systems and centralized booking platforms marked a shift  
toward improved efficiency and interconnectivity (Han et al., 2021). By the 1990s, Property Management  
Systems (PMS) and Centralized Reservation Systems (CRS) became mainstream, automating room assignment,  
occupancy tracking, and front-office communication (Law & Jogaratnam, 2005). However, these early  
applications were operationally focused, emphasizing cost reduction rather than guest experience enhancement.  
The rise of the Internet and Web 2.0 technologies transformed this landscape dramatically. Hotels began to  
leverage digital channels for online bookings, reputation management, and customer engagement (Leung, 2020).  
The current phase; often referred to as Hospitality 4.0, is characterized by the integration of artificial intelligence  
(AI), the Internet of Things (IoT), and data analytics to deliver personalized, automated experiences (Buhalis et  
al., 2024). The COVID-19 pandemic accelerated these transitions as contactless technologies became  
synonymous with hygiene and safety (Wu et al., 2024).  
Modern innovations such as smart-room controls, mobile keys, and voice-activated assistants enable guests to  
tailor temperature, lighting, and entertainment preferences autonomously (Tyagi & Patvekar, 2019). Similarly,  
self-service kiosks have reduced check-in queues while maintaining distancing protocols (Gupta & Sharma,  
2021). Global hotel chains like Hilton, Marriott, and Hyatt have rolled out digital key systems and mobile  
concierge apps to streamline the guest journey (Keymolen, 2017). These developments illustrate a paradigm  
shift from technology as a supporting tool to technology as a core component of the guest experience.  
Despite rapid digitalization, adoption outcomes vary. Many hotels—particularly independent and mid-scale  
properties—struggle with integration costs, staff training, and inconsistent guest acceptance (Montargot &  
Lahouel, 2018). The success of technological implementation therefore depends not only on infrastructure  
investment but also on guests’ readiness and attitudes toward digital interfaces. As such, understanding  
behavioral intention toward hotel technology has become an emerging research priority.  
UTAUT and Technology Acceptance in Hospitality  
The Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by Venkatesh et al. (2003)  
synthesizes eight earlier acceptance models—including TAM, TPB, and DOI—into a unified framework  
comprising four core determinants: performance expectancy (PE), effort expectancy (EE), social influence (SI),  
and facilitating conditions (FC). UTAUT posits that these constructs shape behavioral intention (BI), which in  
turn predicts actual use behavior. The model’s robustness and flexibility have led to its application across diverse  
domains, including education, healthcare, and hospitality (Tao et al., 2019; Ibrahim & Islam, 2024).  
In hospitality research, performance expectancy refers to the degree to which guests believe that using hotel  
technology will enhance their stay experience or service quality (Venkatesh et al., 2003). For instance,  
contactless check-in or mobile applications can save time, reduce uncertainty, and improve satisfaction (Hao,  
2021). Studies indicate that when guests perceive technology as beneficial and efficient, they are more likely to  
engage with it and revisit the property (Ali et al., 2022).  
Effort expectancy, often equated with ease of use, denotes how simple and intuitive a technology is perceived to  
be (Thusi & Maduku, 2020). In hotel contexts, user-friendly mobile interfaces or straightforward self-check-in  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
kiosks encourage guest adoption. When the perceived cognitive load is low, intention to use rises (Ibrahim &  
Islam, 2024).  
Social influence captures the perceived social pressure or encouragement from others to use technology. In  
hospitality, online reviews, influencer recommendations, or peer discussions can shape travelers’ perceptions of  
technology-enabled hotels (Pan et al., 2022). While this variable has shown strong predictive power in  
collectivist cultures, its influence may weaken as digital adoption becomes normative.  
Finally, facilitating conditions encompass the technical and organizational resources that support technology use  
(Venkatesh et al., 2003). For guests, this includes reliable Wi-Fi, clear instructions, and responsive technical  
support (Ibrahim & Islam, 2024). When these conditions are perceived as adequate, users feel more confident  
engaging with digital systems.  
Within hospitality, UTAUT has been extended in several directions. Hao (2021) integrated perceived risk and  
trust to study contactless technology adoption during COVID-19, finding performance and effort expectancy as  
dominant predictors. Davari and Jang (2021) linked UTAUT with the Theory of Planned Behavior to explain  
travel and visit intentions, reinforcing its suitability for tourism behavior research. Yet, despite these applications,  
few studies test generational moderation, even though user perceptions and digital proficiencies are known to  
vary across age cohorts. The current study addresses this omission by examining how Millennials and Generation  
Z differ in how they evaluate the four UTAUT constructs when forming hotel visit intentions.  
Generational Differences in Technology Perception  
The generational cohort theory posits that individuals born within the same historical period share formative  
experiences that influence attitudes, values, and behaviors (Pinto, 2020). In the context of technology adoption,  
these experiences shape expectations about usability, trust, and innovation.  
Millennials (1981–1996) are often characterized by pragmatic technology use. They experienced the emergence  
of the Internet and mobile communication during adolescence and early adulthood, cultivating adaptability but  
also a preference for reliability and structure (Calvo-Porral & Pesqueira-Sanchez, 2019). In hotel contexts,  
Millennials value conveniences such as mobile check-in or online booking, but still appreciate human interaction  
for complex or personalized services.  
Generation Z (1997–2012), sometimes labeled “digital natives 2.0,” has grown up in a world of constant  
connectivity, social media, and algorithmic personalization (Dolot, 2018). They expect speed, automation, and  
seamless transitions between online and offline experiences. Studies show that Generation Z travelers rely  
heavily on mobile applications for planning and booking and are more open to fully automated hotel  
environments (Gorynski, 2024; Seyfi et al., 2024).  
Empirical evidence highlights meaningful differences in technology acceptance across these cohorts. For  
example, Cain et al. (2024) found that Generation Z associates hotel technology with novelty and innovation,  
whereas Millennials associate it with functional convenience. Gupta and Sharma (2021) observed that  
Millennials prioritize clarity of instructions and technical support in self-service kiosks, while Generation Z  
assumes such systems to be self-explanatory. Additionally, social influence manifests differently: Millennials  
respond to recommendations from trusted peers and family, whereas Generation Z is more influenced by online  
communities, influencers, and digital reviews (Shah, 2024).  
These generational dynamics suggest that behavioral intentions toward hotel technologies are not uniform. While  
both cohorts are digitally proficient, their motivations differ—Millennials seek assurance and user-friendliness,  
Generation Z seeks novelty and control. Integrating these insights into the UTAUT framework provides a more  
nuanced understanding of technology adoption in hospitality.  
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METHODOLOGY  
Research Design  
This study adopted a quantitative, cross-sectional research design to examine how the four core constructs of the  
Unified Theory of Acceptance and Use of Technology (UTAUT)—performance expectancy, effort expectancy,  
social influence, and facilitating conditions—affect guests’ visit intentions toward technology-enabled hotels,  
and whether these effects differ between Millennials and Generation Z. Quantitative methods were selected to  
allow statistical testing of hypothesized relationships and to facilitate multigroup comparisons based on  
generational cohorts.  
The research design was non-experimental and correlational, focusing on measuring relationships among  
constructs rather than manipulating variables. Data were collected using a structured online survey to capture  
guests’ perceptions of hotel technologies, consistent with similar studies in hospitality technology adoption (e.g.,  
Hao, 2021; Ibrahim & Islam, 2024). The study followed ethical standards for human participant research,  
ensuring voluntary participation, anonymity, and confidentiality.  
Population and Sampling  
The target population consisted of hotel guests in Malaysia who had stayed in a hotel offering technology-  
enabled services (e.g., mobile check-in, digital key access, smart-room features, or self-service kiosks) within  
the preceding 12 months. This inclusion criterion ensured that all respondents possessed relevant experiential  
knowledge of technology use in a hospitality setting.  
Quota sampling was employed to ensure balanced representation of Millennials (born 1981–1996) and  
Generation Z (born 1997–2012). This non-probability sampling method is appropriate when specific subgroups  
must be proportionately represented for comparative analysis (Saunders et al., 2019). A total of 347 valid  
responses were obtained: 162 from Millennials and 185 from Generation Z.  
Demographic information such as gender, age, educational background, and travel frequency was also collected  
to contextualize the sample. Both generations exhibited a balanced gender ratio and similar hotel usage patterns,  
suggesting comparability between cohorts. The sample size exceeded the minimum threshold of 200 respondents  
recommended for Partial Least Squares Structural Equation Modeling (PLS-SEM) (Hair et al., 2019), ensuring  
adequate statistical power.  
Research Instrument  
The research instrument was a structured questionnaire comprising six sections corresponding to the study  
variables such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating  
Conditions (FC), Visit Intention (VI). Each construct was measured using multiple items adapted from  
established scales in UTAUT and hospitality literature (Venkatesh et al., 2003; Ibrahim & Islam, 2024; Pan et  
al., 2022). All items employed a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree.  
Sample items include: “Using hotel technology makes my stay more efficient” (Performance Expectancy),  
“Hotel technology systems are easy to use” (Effort Expectancy), “People important to me think I should use  
hotel technology” (Social Influence), “I have the necessary support to use hotel technology when needed”  
(Facilitating Conditions), and “I intend to choose hotels with advanced technology in future stays” (Visit  
Intention).  
The questionnaire underwent content validation by three hospitality and tourism researchers to ensure conceptual  
clarity and relevance. A pilot test involving 30 respondents was conducted prior to the main survey. Reliability  
analysis from the pilot yielded Cronbach’s alpha values above 0.80 for all constructs, confirming internal  
consistency. Minor wording adjustments were made for clarity, particularly in distinguishing “hotel technology”  
from general “travel apps.”  
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Data Collection Procedures  
Data collection occurred over a seven-week period through online channels such as Facebook, Instagram, and  
WhatsApp. These platforms were chosen due to their high engagement rates among Millennials and Generation  
Z (Shepherd, 2025). Respondents were provided with a short introduction explaining the study’s purpose and  
assuring anonymity. Screening questions confirmed their eligibility (e.g., age cohort, hotel technology usage  
within the past year).  
To prevent response bias, participation was voluntary and uncompensated. Responses exhibiting straight-lining,  
incomplete data, or unrealistic completion times were excluded, yielding 347 usable cases. The online format  
enabled wide geographical reach across Malaysia while minimizing physical contact, consistent with post-  
pandemic data collection ethics.  
Ethical Considerations  
Ethical integrity was maintained throughout the research process. Participation was voluntary, and respondents  
were informed of their rights to withdraw at any time without penalty. The survey introduction outlined the  
study’s purpose, confidentiality measures, and contact information for inquiries. No personal identifiers (such  
as names, hotel details, or IP addresses) were collected.  
The research complied with the UiTM Research Ethics Guidelines and aligned with the principles of the  
Declaration of Helsinki (2013) regarding anonymity and informed consent. Data were stored securely on  
password-protected systems and used solely for academic purposes.  
RESULTS  
Introduction  
Data analysis was conducted using Partial Least Squares–Multigroup Analysis (PLS-MGA) through the  
SmartPLS software package. PLS-MGA is well suited for this study because it allows simultaneous estimation  
of multiple latent constructs and comparison of structural relationships across distinct groups (Hair et al., 2016).  
The method’s flexibility and predictive orientation make it particularly valuable in examining moderating effects  
such as generational differences within behavioral intention models.  
Overview of Respondent Demographic Characteristics  
After data collection and screening, a total of 347 valid responses were analyzed, consisting of 162 (46.7%)  
Millennials and 185 (53.3%) Generation Z participants. Table 1 summarizes the demographic distribution of  
both cohorts.  
Table 1 The Demographic Characteristics of the Respondents (N = 347)  
Characteristics  
Gender  
Category  
Millennials (n=162)  
Generation Z (n=185)  
Frequency  
%
Frequency  
%
Male  
78  
84  
12  
47  
47  
48.1  
51.9  
7.4  
83  
102  
17  
67  
37  
44.9  
55.1  
9.2  
Female  
Night of stay  
0 night  
1 – 3 nights  
4 – 6 nights  
29.0  
29.0  
36.2  
20.0  
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6 – 8 nights  
36  
20  
22.2  
12.3  
40  
24  
21.6  
13.0  
9+ nights  
Note. This table presents the gender and hotel-stay frequency for respondents from both generational cohorts.  
For gender, the Millennial sample comprised 78 (48.1%) males and 84 (51.9%) females, whereas Generation Z  
included 83 (44.9%) males and 102 (55.1%) females. Overall, the data show a balanced representation across  
gender, with a slightly higher proportion of females among Generation Z.  
Regarding hotel-stay frequency over the past 12 months, Millennials reported 12 (7.4%) respondents with no  
hotel stay, 47 (29.0%) staying 1–3 nights, another 47 (29.0%) staying 4–6 nights, 36 (22.2%) staying 6–8 nights,  
and 20 (12.3%) staying more than 9 nights. Generation Z showed a comparable distribution: 17 (9.2%) with no  
hotel stay, 67 (36.2%) staying 1–3 nights, 37 (20.0%) staying 4–6 nights, 40 (21.6%) staying 6–8 nights, and 24  
(13.0%) staying more than 9 nights. These results indicate similar travel behaviors across generations, although  
Generation Z reported a slightly higher proportion of short stays (1–3 nights).  
Measurement Model Assessment  
To assess the reliability and validity of constructs, composite reliability (CR) and average variance extracted  
(AVE) were calculated. According to Cheung et al. (2024), CR values above 0.70 indicate internal consistency,  
while AVE values above 0.50 demonstrate convergent validity.  
As shown in Table 2, all constructs across both generations exceed these thresholds (CR = 0.964–0.974; AVE =  
0.800–0.848), confirming that the measurement items are both reliable and valid. These results support the  
adequacy of the measurement model for subsequent structural analysis.  
Table 2 Convergent Validity and Reliability  
Generation  
Millennials  
Generation Z  
CR  
AVE  
CR  
AVE  
Performance Expectancy (PE)  
Effort Expectancy (EE)  
Facilitating Condition (FC)  
Social Influence (SC)  
0.967  
0.966  
0.968  
0.964  
0.967  
0.812  
0.810  
0.819  
0.800  
0.813  
0.974  
0.973  
0.974  
0.970  
0.974  
0.848  
0.843  
0.845  
0.825  
0.844  
Visit Intentions (VI)  
Note. This table presents CR and AVE values for each construct for both generational cohorts.  
The measurement model’s quality is further illustrated in Figure 1, which displays the outer loadings for each  
observed variable, all exceeding the recommended threshold of 0.70.  
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Figure 1. Measurement model’s quality.  
Structural Model Evaluation  
Following the establishment of measurement validity, the structural model was tested using a bootstrapping  
procedure with 5,000 resamples to estimate the significance of path coefficients (β) and p-values. Hypotheses  
were accepted when p < 0.05 and rejected when p > 0.05 (Gildeh et al., 2017).  
Millennials  
The structural model for Millennials is presented in Figure 2. The path coefficients are as follows:  
Performance Expectancy → Visit Intention (β = 0.133, p > 0.05)  
Effort Expectancy → Visit Intention (β = 0.360, p < 0.05)  
Social Influence → Visit Intention (β = 0.053, p > 0.05)  
Facilitating Conditions → Visit Intention (β = 0.456, p < 0.05)  
Thus, effort expectancy and facilitating conditions significantly influence Millennials’ visit intentions, whereas  
performance expectancy and social influence do not. Accordingly, H2 and H4 are supported for this cohort, while  
H1 and H3 are rejected.  
Figure 2. Structural model for Millennials.  
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Generation Z  
The structural model for Generation Z is shown in Figure 3 . The coefficients indicate:  
Performance Expectancy → Visit Intention (β = −0.037, p > 0.05)  
Effort Expectancy → Visit Intention (β = 0.524, p < 0.05)  
Social Influence → Visit Intention (β = 0.101, p > 0.05)  
Facilitating Conditions → Visit Intention (β = 0.397, p > 0.05)  
Only effort expectancy exhibits a significant positive relationship with visit intention (p < 0.05). Therefore, for  
Generation Z, H2 is accepted, while H1, H3, and H4 are rejected.  
Figure 3. Structural model for Generation Z.  
Hypothesis Testing Summary  
The summary of direct effects for both generations is provided in Table 3. The Two predictors, effort expectancy  
and facilitating conditions, significantly affect visit intention among Millennials, whereas only effort expectancy  
is significant among Generation Z.  
Table 3 Hypothesis Testing: Direct Effect  
Hypothesis Relationship Millennials  
Generation Z  
Path Coefficient p-value Decision Path Coefficient  
p-value Decision  
H1  
H2  
H3  
H4  
PE à VI  
EE à VI  
SI à VI  
FC à VI  
0.133  
0.360  
0.053  
0.456  
0.275  
0.004  
0.424  
0.000  
Rejected -0.037  
Accepted 0.524  
Rejected 0.101  
Accepted 0.397  
0.437  
0.002  
0.335  
0.082  
Rejected  
Accepted  
Rejected  
Rejected  
Note. This table displays the direct-effect results for each hypothesis across both generational cohorts.  
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Summary of Findings  
Based on the analysis, both Millennials and Generation Z are not significantly influenced by performance  
expectancy or social influence in forming visit intentions toward technology-enabled hotels. However, effort  
expectancy exerts a strong and significant positive effect for both groups, confirming that perceived ease of use  
remains central to technology acceptance.  
For facilitating conditions, only Millennials show a significant positive relationship, suggesting that this  
generation relies more heavily on available support systems and technological infrastructure when engaging in  
hotel technologies. Generation Z, conversely, may assume such support to be implicit, reflecting higher digital  
self-efficacy.  
Collectively, these findings indicate partial generational moderation, where only the facilitating-conditions on  
visit-intention pathway differ significantly between Millennials and Generation Z. This partially supports the  
moderating hypothesis, reinforcing that while ease of use is universally influential, perceptions of support and  
reliability remain generation specific.  
DISCUSSION  
The results reveal meaningful patterns in how Millennials and Generation Z perceive, evaluate, and act upon  
technological innovations in hotel settings. Although both cohorts are digital natives, their distinct formative  
experiences shape different expectations and behavioral intentions. The findings extend the theoretical  
understanding of technology acceptance and offer nuanced insights into generationally differentiated digital  
behavior.  
Effort Expectancy as a Universal Predictor  
The results highlight effort expectancy as the only construct that significantly influences visit intention for both  
Millennials and Generation Z. This finding affirms the central role of perceived ease of use in technology  
acceptance, aligning with the foundational premise of UTAUT (Venkatesh et al., 2003) and corroborating prior  
studies in hospitality contexts (Hao, 2021; Ibrahim & Islam, 2024). Regardless of generational cohort, guests are  
more likely to choose and revisit hotels where technological systems are intuitive, responsive, and frictionless.  
For Millennials, ease of use likely reduces cognitive effort and enhances the sense of control when navigating  
hotel technologies such as self-check-in kiosks or digital concierge applications. This generation values  
technology as a functional enabler that simplifies travel logistics (Calvo-Porral & Pesqueira-Sanchez, 2019). For  
Generation Z, ease of use may instead reflect expectations of technological fluency; a seamless, app-based  
experience that mirrors the immediacy of their social and digital lives (Dolot, 2018). Thus, while both groups  
prioritize usability, the underlying motivations differ: Millennials seek convenience; Generation Z expects  
intuitiveness as a default.  
These findings support the notion that effort expectancy remains a baseline determinant of behavioral intention,  
even in technology-mature environments. In contrast to studies suggesting that ease of use loses relevance once  
users gain digital literacy (Amoako-Gyampah, 2023), this research shows that in hospitality, perceived usability  
continues to drive engagement because it intersects with service quality and emotional comfort. In a high-contact  
service sector, simplicity and clarity of technological interaction become extensions of hospitality itself.  
The Diminished Role of Performance Expectancy  
Contrary to expectations, performance expectancy did not significantly influence visit intention for either  
generation. This finding diverges from earlier research where perceived usefulness was a strong driver of  
technology adoption (Davis, 1989; Pan et al., 2022). A plausible explanation is that hotel technologies are now  
ubiquitous and standardized, leading guests to view them as basic operational features rather than differentiating  
factors. As contactless check-in, mobile keys, and smart-room controls become industry norms, their perceived  
utility may no longer enhance guests’ decision-making.  
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For Millennials, technology’s contribution to performance, such as speed or convenience, might already be  
assumed. For Generation Z, functionality is overshadowed by experiential innovation; they value novelty,  
aesthetics, and personalization more than incremental efficiency gains (Seyfi et al., 2024). This generational  
divergence underscores a broader shift in consumer expectations: technology is no longer judged solely by its  
instrumental value but by its ability to enhance experience and emotion.  
From a theoretical standpoint, these results suggest that in mature digital environments, performance  
expectancy’s predictive power weakens, necessitating an expanded conceptualization of “perceived value” that  
includes hedonic and symbolic dimensions (UTAUT2; Venkatesh et al., 2012). Hospitality researchers should  
thus explore constructs such as enjoyment, trust, and emotional engagement to capture the evolving nature of  
technological acceptance among younger travelers.  
The Limited Influence of Social Influence  
The non-significance of social influence for both generations contrasts with studies conducted in collectivist  
cultures where peer and family opinions often shape behavioral intentions (Pan et al., 2022; Davari & Jang,  
2021). This result may reflect the individualized nature of technology-related decisions in hotel selection. Unlike  
social media or fashion consumption that are highly driven by social validation, hotel technology adoption is  
largely private and utilitarian. Guests interact with hotel systems independently, limiting the relevance of  
interpersonal persuasion.  
Generationally, this outcome also reflects differing socialization patterns. Millennials, though community-  
oriented, rely on trusted peer recommendations rather than public endorsements (Gorynski, 2024). Generation  
Z, by contrast, engages in digital collectivism, where opinions are abundant but filtered through skepticism.  
Their digital literacy enables them to evaluate technologies critically, reducing susceptibility to external  
influence (Shah, 2024). Consequently, social influence loses explanatory strength in contexts where autonomous  
experience and self-efficacy dominate.  
This result extends UTAUT’s theoretical application by demonstrating that the salience of social norms varies  
by behavioral domain. In technology-mediated hospitality, where service interaction is individualized and  
transient, normative pressures may exert minimal effect on decision-making. Future research could consider  
alternative social constructs such as electronic word-of-mouth (eWOM) credibility or online trust to capture  
subtle social dynamics in digital hospitality ecosystems.  
Facilitating Conditions and the Generational Divide  
The results reveal that facilitating conditions significantly affect visit intention for Millennials but not for  
Generation Z, supporting partial generational moderation. This distinction underscores the evolving meaning of  
“support” and “infrastructure” across age cohorts.  
For Millennials, the presence of reliable Wi-Fi, clear usage instructions, and responsive technical support  
enhances confidence in hotel technologies. They value the reassurance that assistance is available should  
technological issues arise. This aligns with previous findings suggesting that Millennials prefer a hybrid service  
model, blending automation with the human touch (Gupta & Sharma, 2021). Hotels that visibly maintain support  
systems and staff readiness foster trust among this cohort.  
In contrast, Generation Z’s lack of sensitivity to facilitating conditions reflects their inherent digital self-efficacy.  
Having grown up with intuitive technology, they expect systems to function seamlessly and often interpret the  
need for assistance as a sign of poor design (Dolot, 2018). This generation prizes autonomy, perceiving external  
support as unnecessary unless technology fails entirely. Their emphasis on self-navigation parallels trends in  
mobile banking, e-learning, and travel apps, where Generation Z users exhibit low dependence on formal  
assistance (Cain et al., 2024).  
This finding reinforces that facilitating conditions operate as a generationally contingent factor. For older digital  
natives, they represent an enabling resource; for younger ones, they signify redundancy. Practically, hotel  
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managers must tailor technological ecosystems accordingly providing visible support channels for Millennials  
while focusing on minimalist, user-driven design for Generation Z. From a theoretical angle, this supports the  
integration of technology readiness and self-efficacy into future adaptations of UTAUT within hospitality  
contexts.  
Partial Generational Moderation  
The multigroup analysis (PLS-MGA) indicated partial moderation: the path between facilitating conditions and  
visit intention significantly differs between generations, whereas other relationships remain stable. This partial  
moderation suggests that while the core mechanisms of technology acceptance (e.g., ease of use) are consistent  
across cohorts, the contextual interpretation of these constructs diverges.  
Such findings echo the argument of Venkatesh et al. (2012) that demographic moderators—including age and  
experience—affect the salience of different determinants. In hospitality, generationally distinct cognitive  
schemas influence how users perceive risk, convenience, and reliability. Millennials prioritize structured  
functionality; Generation Z emphasizes fluid usability. Consequently, technology acceptance in hotels cannot be  
assumed homogeneous, even among digitally proficient users.  
From a methodological standpoint, the robustness of PLS-MGA supports the study’s theoretical contribution: by  
empirically validating generational variation within UTAUT, the research advances a contextualized  
understanding of digital behavior in hospitality—one that accounts for temporal, cultural, and demographic  
heterogeneity.  
Theoretical Implications  
This study extends existing literature in several ways. First, it demonstrates that UTAUT remains relevant in  
hospitality but requires adaptation to account for generational and experiential nuances. The universal influence  
of effort expectancy affirms the model’s foundation, while the reduced role of performance expectancy and  
social influence suggests saturation effects in technologically mature service environments.  
Second, the results highlight that facilitating conditions are generationally dependent, expanding UTAUT’s  
explanatory boundary by linking it to technology readiness and self-efficacy frameworks. This generational  
contextualization enriches theoretical models that traditionally treat users as homogenous.  
Third, the research reinforces that behavioral intention toward hotel technology is not merely a rational  
evaluation of usefulness but also a reflection of experiential alignment, how technology fits users’ lifestyles,  
expectations, and identity. This insight invites integration of constructs such as hedonic motivation, habit, and  
trust from UTAUT2 and the Technology Readiness Index to build more comprehensive predictive models for  
hospitality technology adoption.  
Managerial Implications  
The practical implications are equally significant. For hotel managers, the findings underscore the importance  
of usability design and digital inclusivity. Since effort expectancy drives intention for both generations, hotels  
should prioritize intuitive interfaces, minimal steps, and responsive functionality in their technology systems.  
Self-service kiosks, mobile apps, and digital room controls should be designed with consistent navigation logic  
and clear prompts.  
For Millennials, visible support mechanisms, such as on-screen guidance, chat assistance, or front-desk  
troubleshooting, enhance confidence and satisfaction. Marketing communications should emphasize reliability,  
customer support, and seamless problem resolution.  
For Generation Z, the focus should shift to personalization and autonomy. This cohort appreciates mobile-first  
platforms that integrate loyalty programs, social-sharing features, and real-time customization. Their loyalty is  
earned through experiential engagement rather than service reassurance. Incorporating gamification orAI-driven  
recommendations can further strengthen their connection to hotel brands.  
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Strategically, hotels should recognize that digital transformation is not purely technological but behavioral. By  
segmenting users based on generational needs, managers can optimize return on technology investments,  
improve satisfaction, and enhance revisit intentions.  
In summary, this study reveals both convergence and divergence across generational cohorts in their responses  
to hotel technology. Both Millennials and Generation Z emphasize ease of use as a critical determinant of visit  
intention, confirming that user-friendly design is central to digital hospitality. However, their expectations  
diverge regarding infrastructural support as Millennials value visible assistance, while Generation Z presumes  
technological competence and autonomy.  
The absence of significant effects for performance expectancy and social influence underscores the  
normalization of technology in hospitality: what was once an innovation is now an expectation. As hotels  
continue to digitize, future differentiation will depend less on the presence of technology and more on its  
integration into meaningful guest experiences.  
Collectively, the discussion positions this study as an important step toward understanding how digital  
transformation intersects with generational behavior in hospitality, offering both theoretical enrichment and  
actionable managerial insight.  
IMPLICATIONS AND CONCLUSION  
Theoretical Implications  
This study advances understanding of technology adoption in hospitality by applying and contextualizing the  
Unified Theory of Acceptance and Use of Technology (UTAUT) across generational cohorts. The findings  
reaffirm that effort expectancy remains the strongest and most consistent predictor of visit intention among both  
Millennials and Generation Z. Regardless of age, guests are more inclined to revisit hotels when digital systems  
are intuitive, seamless, and easy to navigate, confirming that usability remains central to technology acceptance  
even among digitally literate users.  
Conversely, performance expectancy and social influence no longer significantly influence visit intentions,  
suggesting a shift in perception as hotel technologies become normalized. Guests may now take functionality  
and usefulness for granted, focusing instead on experience, quality and personalization. This finding supports  
extending UTAUT with constructs from UTAUT2, such as hedonic motivation and trust, to better reflect post-  
adoption behavior in hospitality.  
The only construct showing generational moderation was facilitating conditions, significant for Millennials but  
not for Generation Z. Millennials still value visible infrastructural support, while Generation Z expects  
technology to be inherently reliable and self-guided. This distinction introduces a generational dimension to  
UTAUT, linking it to technology readiness and self-efficacy theory, and showing that enabling factors are  
interpreted differently across cohorts.  
Managerial Implications  
The findings provide several practical insights for hotel operators striving to align technological innovation with  
guest expectations. Since effort expectancy emerged as the most consistent determinant of visit intention, hotels  
must prioritize usability and simplicity in every technological interface. Digital check-in systems, room controls,  
and mobile applications should be designed to be intuitive, visually coherent, and responsive to minimize guest  
effort. For Millennials, visible technological support—such as staff assistance, clear instructions, and responsive  
troubleshooting—remains important for building trust and confidence in digital systems. In contrast, Generation  
Z values autonomy and expects seamless, self-directed experiences that mirror their broader digital lifestyles.  
This cohort prefers mobile-first platforms that enable personalization, gamified engagement, and quick access  
without human intervention. Accordingly, managers should adopt differentiated digital strategies, balancing  
automation with human touchpoints depending on the generational profile of their clientele. Furthermore,  
technology in hotels should no longer be treated merely as a back-end efficiency tool but as a strategic element  
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of service experience design. When digital systems are integrated with emotional engagement, aesthetic appeal,  
and brand storytelling, they enhance not only satisfaction but also long-term loyalty. To sustain these innovations,  
staff training must emphasize digital empathy—equipping employees to guide guests through technology with  
confidence and warmth. Collectively, these managerial insights position technology not as a replacement for  
hospitality but as a means to redefine guest experience through seamless digital–human synergy.  
Limitations and Future Research  
The study’s non-probability sampling limits generalizability, though balanced generational representation adds  
robustness. Future research could employ stratified or longitudinal designs to explore how perceptions evolve  
with technological maturity. Including variables such as trust, risk perception, and hedonic value could enrich  
understanding of post-pandemic digital hospitality behavior. Comparative studies across regions or cultures  
would further test the generality of the findings.  
Conclusion  
This research demonstrates that while both Millennials and Generation Z value technological ease of use, their  
responses to infrastructural support differ. Millennials’ visit intentions are strengthened by visible assistance,  
whereas Generation Z’s are driven by autonomy and intuitive design. The results refine UTAUT’s applicability  
in hospitality and guide managers toward adaptive, generation-sensitive digital ecosystems. As hotels advance  
into a new era of smart hospitality, competitive success will depend not on what technology is implemented, but  
how meaningfully it enhances the guest experience—balancing digital efficiency with the human warmth that  
defines true hospitality.  
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