Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 771 https://internationalpubls.com Navigating the Future of Hospitality: Understanding Guest Adoption of Contactless Services Sitesh Srivastav1, Gaurav Bathla1, Amit Kumar2* 1 School of Hotel Management, Airlines and Tourism, CT University, Ludhiana, Punjab, India 2 Central University of Haryana Email: ak.amitmathur@gmail.com Article History: Received: 12-12-2024 Revised: 25-01-2025 Accepted: 05-02-2025 Abstract: Using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) paradigm, the study examines customer attitudes and adoption patterns for contactless services in the hospitality sector within the Tricity region (Chandigarh, Panchkula, and Mohali). Given the COVID-19 pandemic and escalating health concerns, the hotel industry is rapidly adopting contactless technology. Therefore, it is imperative to comprehend the aspects that impact visitor adoption. A sample of 500 respondents from the Tricity region were given self-administered questionnaires as part of this quantitative/descriptive study design. The study looks at how behavioural intentions and contactless service use behaviour are affected by the UTAUT2 components (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Habit, Hedonic Motivation, and Price Value). The findings show that Behavioural Intentions, which greatly impact Use Behaviour, are highly influenced by Facilitating Conditions, Habit Behaviour, Performance Expectancy, and Price Value. On the other hand, it was discovered that Behavioural Intentions were not significantly impacted by Effort Expectancy, Social Influence, or Hedonic Motivation. According to these results, the most successful adoption methods should emphasise improving the environment that facilitate adoption, promoting consistent use, showcasing the obvious benefits of the technology, and guaranteeing value for money. For hotel operators looking to maximise their contactless service offerings and raise visitor happiness, the report offers practical insights. Keywords: Contactless Services, UTAUT2 Framework, Behavioral Intentions, Facilitating Conditions, Hospitality Industry. 1. INTRODUCTION The COVID-19 epidemic has caused significant shifts in consumer behaviour in a number of industries, including the hospitality sector. The sector has to quickly change in order to guarantee guest safety while upholding the calibre of service delivery when the necessity to reduce health risks became critical (Gretzel et al.,2020). Among these modifications, contactless services have become a standout invention that aims to improve operational effectiveness and handle safety issues. These services cover a variety of technological innovations that enhance the smooth and hygienic visitor experience, such as digital room keys, touchless payments, mobile check-ins, and smart room controls (Al-Ansi et al., 2021). mailto:ak.amitmathur@gmail.com Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 772 https://internationalpubls.com In order to reduce in-person interactions and stop the spread of contagious diseases, hotels and other lodging establishments now strategically need to introduce contactless services (Aguiló et al., 2021). Increased visitor awareness of hygiene and a desire for fewer in-person touchpoints when interacting with hospitality services are the main factors driving the growing demand for these services (Chathoth et al., 2021). Furthermore, contactless technologies' efficiency, ease, and perceived safety have all greatly boosted their appeal, turning them from a transient solution into a long-term fixture in the hospitality sector (Sigala et al., 2020). In order to match their products with changing customer expectations, hotels must comprehend visitor attitudes and adoption patterns regarding contactless services. The simplicity of use (effort expectancy), perceived usefulness (performance expectancy), availability of supporting infrastructure (facilitating conditions), and the perceived hedonic and financial value by guests are the elements driving visitor adoption of these technologies (Hao, 2021). In the end, the analysis of these variables helps hotels improve service quality, fortify brand connections, and raise overall guest happiness by offering insightful knowledge about the dynamics of visitor behaviour and preferences (Hao & Chon, 2021). The purpose of this paper is to investigate the intricate relationships that exist between visitor attitudes, preferences, and adoption trends with regard to contactless services in the hotel sector. Through an examination of the experiences of visitors interacting with these technologies in different hotels across India, the study aims to provide a thorough knowledge of the elements that propel the uptake of contactless services. By means of this investigation, the study will add to the current conversation about the future of hospitality by offering industry stakeholders practical guidance as they traverse the changing terrain of guest service provision. 2. REVIEW OF LITERATURE AND RESEARCH GAP A number of frameworks and theories have been thoroughly examined in the literature on the acceptance of contactless services in the hospitality industry, with the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) having a particularly significant impact. (Venkatesh et al, 2012) developed UTAUT2, an extension of the original UTAUT model that includes dimensions such as Effort Expectancy, Performance Expectancy, Hedonic Motivation, Price Value, Facilitating conditions, Social Influence, and Habit. These concepts have been extensively employed to comprehend how people behave with new technologies in a variety of contexts, such as the hospitality industry. Within this framework, UTAUT2 offers a strong theoretical basis to evaluate how visitors' expectations of functionality, convenience of use, social influence, and regular technology use impact their views and intents. Research has continuously demonstrated that these variables have a major impact on hotel guests' adoption of contactless services, which makes UTAUT2 an essential model for comprehending technological acceptance in this industry. Through the emphasis on the effects of technical advancements on visitor experiences and preferences, empirical research have contributed to our knowledge of the uptake of contactless services. (Fan et al., 2022; Rahimizhian & Irani,2021), for example, looked at how contactless services and in-room technologies have changed guest expectations, highlighting the value of convenience, ease of use, and improved safety. Studies by (Pillai et al., 2021; & Cheung et al., 2021) show that the pandemic has Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 773 https://internationalpubls.com hastened the use of these technologies and highlight the industry's quick change towards contactless services in response to growing health and safety concerns. The applicability of UTAUT2 components in this context has been further validated by research conducted by Yoganathan et al., (2021) and Hao & Chon (2021) that explores the influence of automated systems and contactless service offers in improving consumer trust, contentment, and overall experience. Furthermore, the empirical research has investigated how environmental sustainability and technological readiness may affect visitor behaviour in more general ways. González et al.,, (2020) and Blut & Wang, (2020), for instance, emphasised how consumer decisions, such as whether or not to pay for eco-friendly lodging, are influenced by technological preparedness and environmental consciousness. These studies give important insights into how hotels may use environmental and technology advancements to improve client experiences and loyalty, and match their service offerings with changing visitor preferences. All things considered, the combination of UTAUT2 and these empirical results provides a thorough framework for comprehending and forecasting visitor acceptance trends of contactless services in the hospitality sector. There is still a great deal to learn about how UTAUT2 constructions affect contactless service acceptance in the Tricity region (Chandigarh, Panchkula, and Mohali), despite the wealth of data on guest preferences, behaviours, and the effects of technological improvements in hospitality. Although earlier research has looked at how technology is being adopted in the hospitality industry, it has mainly ignored the particular integration of UTAUT2 in this particular regional setting. By examining how UTAUT2 constructs influence visitor attitudes and contactless service adoption patterns, this study aims to close this knowledge gap and provide important new insights into the relationship between technology adoption theories and visitor behaviour in the Tricity region's hospitality sector. 3. STATEMENT OF PROBLEM AND OBJECTIVE OF THE STUDY The rapid growth of technology and growing worries about health and safety, especially in the wake of the COVID-19 epidemic, have caused a considerable shift in the hotel business towards contactless services. Even though the use of these services has expanded, there is still a significant knowledge vacuum on the variables influencing visitor acceptance of contactless technologies, particularly in places like the Tricity area (Chandigarh, Panchkula, and Mohali). The limited integration of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework in current research in this context exacerbates this gap even more. Hotel operators find it difficult to predict and influence guest adoption patterns unless they have a thorough understanding of how UTAUT2 constructs (e.g., Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Habit, Hedonic Motivation, and Price Value) affect guest behaviour. 4. CONCEPTUAL MODEL OF THE STUDY This study's conceptual model provides a framework for investigating the connections among several elements affecting visitors' perceptions of and usage of contactless services in the hospitality sector. The model, which is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2), integrates important concepts and looks at how they affect visitors' behavioural intentions and actual use behaviour. These include performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. By outlining these connections, the model Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 774 https://internationalpubls.com offers a methodical framework for comprehending the ways in which these variables interact, providing valuable information on the elements that promote or impede the uptake of contactless services in a post-pandemic environment. Thus, the goal of this conceptual model is to offer a thorough knowledge of the dynamics influencing the uptake of contactless services, directing research in academia as well as real-world implementations in the hospitality industry. Figure 1 : Conceptual Framework of The Study 5.VARIABLES USED IN THIS STUDY: Performance Expectancy (PE): PE stands for visitors' expectations in the context of hospitality with reference to the perceived value and advantages of contactless services. It concerns how visitors believe that by providing convenience, effectiveness, and better service quality, contactless services (such as smartphone check-ins, digital room keys, and touchless purchases) would improve their hotel stay. Effort Expectancy (EE): The term "EE" describes how visitors feel about how simple it is to use contactless services. It has to do with how easy, how user-friendly, and how much work it is thought to interact with modern technologies. It evaluates how visitors feel about adopting and using contactless services, which don't need a lot of work or complexity. Social Influence (SI): SI looks at how social factors affect visitors' choices to use contactless services. Peer recommendations, the views of powerful people, and cultural norms are some of the factors that shape how visitors feel about and use contactless services in a hospitality setting. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 775 https://internationalpubls.com Facilitating Conditions (FC): FC assesses the tools and assistance that visitors can use to use contactless services. It involves the technological framework, direction, accessibility, and assistance that hotels offer, all of which make it easier for visitors to use and embrace contactless services. Hedonic Motivation (HM): HM stands for the experience or emotional reasons why customers use contactless services. It investigates the delight, surprise, and pleasure that visitors have when interacting with contactless technologies in a hospitality setting. It evaluates the pleasure and experiential value gained from utilising these services. Price Value (PV): PV is a reflection of how visitors feel about the cost-benefits or ROI of utilising contactless services. It involves the opinions of visitors regarding the financial savings, cost- effectiveness, or added value that these technologies provide in comparison to more conventional approaches in the hospitality industry. Habit Behavior (HB): HB investigates the customary behaviours and habits of visitors about the use of contactless services. It looks at how much frequent use of contactless services by visitors has become automatic and engrained in their interactions with hospitality services. Behavioral Intention (BI): In a hospitality sector, BI indicates how willing and intending guests are to interact with contactless services. It represents visitors' stated goals and reasons for using these services, which are impacted by the previously mentioned UTAUT2 factors. Use Behavior (UB): UB is an indicator of how much customers in the hospitality sector actually use contactless services. The behaviour of customers interacting with these technologies during their hotel stays is depicted, which reflects the adoption and practical application of contactless services. 6. HYPOTHESIS OF THE STUDY H1: There is no significant impact of UTAUT2 constructs on Behavioral Intentions. According to this hypothesis, customer’s behavioural intentions towards adopting contactless services in the hospitality business are not significantly influenced by the UTAUT2 constructs, which include Performance Expectancy, Effort Expectancy, Social Influence, Price Value, Hedonic Motivation, Habit and Facilitating Conditions. In essence, it implies that these variables might not be very important in determining whether visitors plan to accept or utilise these services. It is important to test this hypothesis because it will reveal whether or not the UTAUT2 theoretical framework makes sense when applied to contactless services. If the hypothesis is wrong, it means that UTAUT2 constructs have an impact on behavioural intentions. This validates the construct’s applicability and directs future research or real-world initiatives to improve the constructs' ability to influence guest intentions. H2: There is no significant impact of Behavioral Intentions on Actual Usage Behavior. According to this hypothesis, contactless service user’s actual usage behaviour is not substantially predicted by their behavioural intentions. In the event that the hypothesis is incorrect, it highlights how behavioural intentions accurately predict actual usage and how improving these intentions is essential to increasing service uptake. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 776 https://internationalpubls.com 7. RESEARCH METHODOLOGY To accomplish the study's goals, a quantitative/descriptive research design was used. It was decided that this approach would work well for examining different aspects and acquiring quantitative descriptions of participant attitudes and opinions. Utilising a descriptive study approach and standardised questionnaires for data collection, the study adhered to Creswell's, (2004) criteria. The main survey tool utilised to systematically examine visitor’s attitudes and actions about the use of contactless services in the hotel sector was self-administered questionnaires. A purposive/convenience sampling strategy was selected because of practical restrictions, including time and financial constraints. With participants having experiences at hotels all throughout India, this strategy enabled the selection of a representative sample from the Tricity region, which includes Chandigarh, Panchkula, and Mohali. The goal of the sampling strategy was to get a representative and varied sample of visitor experiences and viewpoints. The sample size was first determined to be 385 using the method for finite populations, taking into account the 1,611,770 finite population in the Tricity region, with a margin of error of 0.05 and a confidence level of 95%. In an effort to improve the data's dependability, 500 respondents were added to the sample. By using this method, a thorough analysis was guaranteed, and insightful information about visitor attitudes, preferences, and adoption trends for contactless services was obtained. 8. RESULTS AND DISCUSSIONS: Table 1: Respondents Profile Count Percentage % Gender Male 359 71.8 Female 141 28.2 Age 18-25 years 55 11.0 26-40 years 217 43.4 41-50 years 151 30.2 Above 50 years 77 15.4 Marital Status Single 71 14.2 Married 429 85.8 Qualification 12 or equivalent 18 3.6 Graduation 73 14.6 Post-graduation 341 68.2 Doctorate 68 13.6 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 777 https://internationalpubls.com Occupation Business/self- employed 130 26.0 Salaried 336 67.2 Student 34 6.8 Monthly Income Below 50000 72 14.4 50000 to 80000 143 28.6 80000 to 100000 115 23.0 Above 100000 152 30.4 Dependent on family Members 18 3.6 A thorough profile of the study participants is given in Table 1. Of the individuals involved, 28.2% were female and 71.8% were male. According to the age distribution, 11.0% of people were between the ages of 18 and 25, 30.2% were between the ages of 41 and 50, and 43.4% were between the ages of 26 and 40. Of the respondents, 14.2% were single and 85.8% were married. The distribution of educational qualifications was as follows: 68.2% had a post-graduate degree, 14.6% had graduated, 13.6% had a doctorate, and 3.6% had a qualification equal to a 12th grade education. In terms of occupation, there were 67.2% paid workers, 26.0% business/self-employed workers, and 6.8% students. According to data on monthly income, 30.4% of respondents made over INR 100,000, 28.6% made between INR 50,000 and INR 80,000, 23.0% made between INR 80,000 and INR 100,000, and 14.4% made less than INR 50,000. Additionally, 3.6% of respondents were dependent on family members. The respondents in this profile are a varied, primarily well-educated, and financially secure population. The validity and reliability of the constructs must be evaluated using confirmatory factor analysis (CFA), and factor loadings must be interpreted in compliance with predefined threshold constraints. Factor loadings show the link between each measuring item and its underlying construct. Higher loadings indicate how effective the item is as a build indicator. A typical benchmark value is 0.70. Items with loadings above this threshold are considered trustworthy indicators of their respective constructions, according to Hair et al. (2019). This threshold ensures a meaningful relationship between the elements and the construct they are intended to measure. Figure 2 : Measurement Model of Study Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 778 https://internationalpubls.com Table 2 : Factor Loadings Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 779 https://internationalpubls.com Estimate PE_1 <--- Performance Expectancy (PE) 0.759 PE_2 <--- Performance Expectancy (PE) 0.746 PE_3 <--- Performance Expectancy (PE) 0.772 PE_4 <--- Performance Expectancy (PE) 0.788 EE _1 <--- Effort Expectancy (EE) 0.795 EE _2 <--- Effort Expectancy (EE) 0.83 EE _3 <--- Effort Expectancy (EE) 0.793 EE _4 <--- Effort Expectancy (EE) 0.799 SI_1 <--- Social Influence (SI) 0.819 SI_2 <--- Social Influence (SI) 0.757 SI_3 <--- Social Influence (SI) 0.766 SI_4 <--- Social Influence (SI) 0.839 FC_1 <--- Facilitating Conditions (FC) 0.782 FC_2 <--- Facilitating Conditions (FC) 0.832 FC_3 <--- Facilitating Conditions (FC) 0.828 FC_4 <--- Facilitating Conditions (FC) 0.789 HB_1 <--- Habit Behavior (HB) 0.767 HB_2 <--- Habit Behavior (HB) 0.786 HB_3 <--- Habit Behavior (HB) 0.721 HB_4 <--- Habit Behavior (HB) 0.744 BI_1 <--- Behavioral Intention (BI) 0.716 BI_2 <--- Behavioral Intention (BI) 0.839 BI_3 <--- Behavioral Intention (BI) 0.806 BI_4 <--- Behavioral Intention (BI) 0.817 HM_1 <--- Hedonic Motivation (HM) 0.855 HM_2 <--- Hedonic Motivation (HM) 0.805 HM_3 <--- Hedonic Motivation (HM) 0.848 PV_1 <--- Price Value (PV) 0.838 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 780 https://internationalpubls.com PV_2 <--- Price Value (PV) 0.771 PV_3 <--- Price Value (PV) 0.834 UB_1 <--- Use Behavior (UB) 0.817 UB_2 <--- Use Behavior (UB) 0.745 UB_3 <--- Use Behavior (UB) 0.807 Hedonic motivation, price value, use behaviour, social influence, performance expectation, effort expectation, and facilitating conditions, in particular, all have loadings that significantly surpass this cutoff point, indicating that they are trustworthy indicators of the corresponding constructs. Hedonic Motivation and Performance Expectancy, for example, have loadings that range from 0.805 to 0.855 and 0.746 to 0.788, respectively, indicating their effective measurement. This analysis confirms that the variables included in the study are appropriately measured based on the threshold limits, and it provides a solid foundation for future research into the influence of these constructs on visitors' actual usage behaviour as well as their behavioural intentions. Table 3 : Reliability Statistics CR AVE MSV MaxR(H) Cronbach's Alpha Behavioral Intention 0.873 0.633 0.247 0.879 0.852 Performance Expectancy 0.851 0.587 0.523 0.851 0.838 Effort Expectancy 0.880 0.647 0.523 0.881 0.736 Social Influence 0.873 0.634 0.358 0.878 0.721 Facilitating Conditions 0.883 0.653 0.396 0.884 0.802 Habit Behavior 0.820 0.533 0.396 0.823 0.850 Hedonic Motivation 0.875 0.699 0.312 0.877 0.748 Price Value 0.856 0.664 0.312 0.859 0.744 Use Behavior 0.833 0.625 0.331 0.837 0.832 Table 3 shows the reliability statistics for the study's constructs and shows that most measures are either above or approach predefined thresholds for validity and reliability. The Composite Reliability (CR) values, which vary from 0.820 to 0.883 and are above the recommended threshold of 0.70 (Hair et al., 2014), demonstrate strong internal consistency across constructs. The Average Variance Extracted (AVE) values, which range from 0.533 to 0.699 and are over the 0.50 threshold, validate adequate convergent validity (Fornell & Larcker, 1981). Cronbach's Alpha values, which are above the 0.70 threshold and range from 0.721 to 0.852, provide additional evidence of the reliability of the used scales (Nunnally & Bernstein, 1994). Discriminant validity is confirmed for each notion by the Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 781 https://internationalpubls.com Maximum Shared Variance (MSV) being smaller than the AVE. The combined validity and reliability assessments, which ensure that the constructs are measured accurately and consistently, reinforce the study's measuring approach's robustness. Table 4 : Discriminant Validity BI PE EE SI FC HABIT HM PV UB BI 0.796 PE 0.477 0.766 EE 0.415 0.723 0.804 SI 0.317 0.379 0.241 0.796 FC 0.318 0.336 0.327 0.578 0.808 HABIT 0.344 0.445 0.318 0.598 0.629 0.730 HM 0.497 0.392 0.404 0.266 0.282 0.371 0.836 PV 0.319 0.289 0.382 0.323 0.310 0.373 0.559 0.815 UB 0.288 0.503 0.454 0.502 0.575 0.509 0.296 0.311 0.790 By comparing each construct's square root of the Average Variance Extracted (AVE) to its correlations with other constructs in Table 4, the discriminant validity of the study's constructs is assessed. The diagonal of the matrix shows the square root of the AVE for each construct; the values range from 0.730 to 0.836, all of which are more than the 0.70 cutoff criterion proposed by Fornell & Larcker (1981). The fact that each construct has more variation with its own indicators than with other constructs confirms strong discriminant validity. The square root of the AVE for behavioural intention (BI) is 0.796, which is lower than its correlations with other dimensions like performance expectancy (PE) at 0.477 and effort expectancy (EE) at 0.415. These results demonstrate the robustness of the measurement model and its ability to discriminate between constructs by indicating that the constructs differ from one another. Table 5 : Model Fit Indices CMIN/Df 2.63 <3 Very good; <5 acceptable CFI 0.938 >.90 good fit TLI 0.926 >.90 good fit IFI 0.939 >.90 good fit RFI 0.912 >.90 good fit NFI 0.913 >.90 good fit RMSEA 0.046 <.08 acceptable, <.05 very good Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 782 https://internationalpubls.com Several major indices show that the model has a strong overall fit. A favourable balance between model complexity and fit is shown by the Chi-Square Minimum Discrepancy per Degree of Freedom (CMIN/DF) value of 2.63, which is far below the 3.00 threshold. The model appears to describe the data better than a null model, as evidenced by the Comparative Fit Index (CFI) and Incremental Fit Index (IFI) values of 0.938 and 0.939, respectively, both of which are over the 0.90 threshold. A high level of explanatory power is indicated by the Tucker-Lewis Index (TLI) and Relative Fit Index (RFI) values, which are 0.926 and 0.912, respectively, and surpass the 0.90 limit. The model fits the data better than the null model, as indicated by the Normed Fit Index (NFI) of 0.913. Last but not least, the approximation error is minimal and the overall adequacy of the model is supported by the Root Mean Square Error of Approximation (RMSEA) value of 0.046, which is below the 0.08 standard. Figure 3: Causal Model of Study Table 6 : Hypothesis Testing of Model Estimate S.E. C.R. P Behavioral Intentions <--- Effort Expectancy 0.039 0.033 1.183 0.237 H1 Behavioral Intentions <--- Social Influence -0.017 0.038 -0.439 0.661 H2 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 783 https://internationalpubls.com Behavioral Intentions <--- Facilitating Conditions 0.123 0.048 2.589 0.010 H3 Behavioral Intentions <--- Habit Behavior 0.407 0.039 10.575 0.000 H4 Behavioral Intentions <--- Performance Expectancy 0.262 0.044 5.917 0.000 H5 Behavioral Intentions <--- Price Value 0.178 0.049 3.631 0.000 H6 Behavioral Intentions <--- Hedonic Motivation 0.024 0.037 0.65 0.516 H7 Use Behavior <--- Behavioral Intentions 0.742 0.039 18.844 0.000 H8 The findings of the hypothesis test provide valuable insights into the variables affecting visitors' Behavioural Intentions and Use Behaviour with regard to contactless services in the hotel sector. The p-values for Hypotheses H1 and H2, which investigated the effects of Social Influence and Effort Expectancy on Behavioural Intentions, were not significant (0.237 and 0.661, respectively). This shows that visitors' intentions in this situation are not greatly impacted by the perceived ease of use of contactless services (Effort Expectancy) or the social pressure to embrace these services (Social Influence). However, a significant p-value of 0.010 supported Hypothesis H3, which looked at the function of Facilitating Conditions. This means that visitors' intentions to use contactless services are positively influenced by the availability of resources and supportive infrastructure. Likewise, Habit Behaviour (H4), which showed a p-value of 0.000, indicated a substantial and significant influence on Behavioural Intentions, highlighting the fact that visitors who are used to similar technologies are more likely to plan to employ contactless services. With p-values of 0.000 for both hypotheses, additional research shows that Performance Expectancy (H5) and Price Value (H6) are significant drivers of Behavioural Intentions. This implies that visitors are more likely to form plans to use contactless services if they believe they are helpful and offer good value for the money. A p-value of 0.516, on the other hand, suggests that Hypothesis H7, which looked at the impact of hedonic motivation, was not supported, indicating that guests' behavioural intentions are not significantly influenced by the pleasure or delight they gain from using contactless services. Lastly, with a p-value of 0.000, Hypothesis H8, which connects Behavioural Intentions to Use Behaviour, was found to be strongly supported, suggesting that visitors' intentions are a reliable indicator of their actual use of contactless services in the hospitality sector. These results demonstrate how practical factors—like usability, value, and ingrained habits—are more important in influencing the adoption of contactless services than social or hedonistic ones. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 784 https://internationalpubls.com 9. CONCLUSIONS AND MANAGERIAL IMPLICATIONS The results of the study highlight how crucial it is to consider facilitating factors, habit behaviour, performance expectancy, and pricing value when predicting behavioural intentions towards contactless services. Behavioural intentions therefore have a strong impact on actual usage behaviour. These insights show that improving visitor acceptance of contactless services in the hospitality sector requires creating favourable conditions, encouraging habitual usage, exhibiting high performance value, and providing perceived value for money. Hotel operators should prioritise, from a managerial standpoint, enhancing favourable conditions like dependable technology and user-friendly interfaces, encouraging guests to utilise contactless payment methods on a regular basis, stressing the convenience and better performance of these services, and making sure that prices are competitive enough to match the perceived value of their offerings. The results indicate that there is a need for specialised tactics that address the specific drivers of guest adoption, since the non-significant influences of effort expectation, social influence, and hedonic motivation show that these elements may not be as effective in this setting. Future studies should examine the complex relationships that exist between these dimensions and the characteristics of guests in various geographical areas. Longitudinal studies might also be conducted to determine the long-term effects of new technology and changing guest preferences on adoption. 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