Consumer Behaviour on AI Applications for Services: Measuring the Impact of Value-Based Adoption Model on Luxurious AI Resorts’ Applications
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Dimitra Skandali
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Anastasios Magoutas
Abstract
Focussing on consumer behaviour analysis derived from the changes in Information and Communications Technology (ICT), the purpose of this study is to analyse the primary content factors that influence consumers’ attitudes and behavioural intentions in the hospitality industry. The present study is the first to investigate how benefits (happiness and perceived immersion) and sacrifices (trust and changes in habits) can predict consumers’ attitudes of acceptance and willingness to pay for artificially intelligent (AI) luxurious resort applications (apps). The researchers employed structural equation modelling to analyse the relationship between technology adoption and specific factors that influence customers’ perceived value in the hospitality industry. The research aims to expand on the theory of the Value Adoption Model (VAM). Based on the findings, AI-powered apps for high-end resorts have a tendency to boost tourists’ confidence and willingness to use and pay for these apps, as well as increase their perceived value. Happiness has an impact on behavioural intentions, while perceived immersion and changes in habits influence the outcomes related to intentions to ultimately accept and purchase them. The findings can benefit both ICT and the hospitality industry. Managers in the ICT industry should collaborate with researchers in service management who are exploring the challenges of technology adoption. Managerial implications and recommendations for future research are extensively provided.
1 Introduction
Recently, the use of artificially intelligent (AI) applications (apps) owned by resorts has greatly increased the popularity of luxurious facilities in travel entertainment. The growing prevalence of AI apps in the tourism sector has given rise to a novel field of study that centres on the importance of expertise concerning the substantial influence of AI apps on tourism marketing. Various aspects of AI technology in the hospitality industry have gained a lot of interest from researchers due to the previous progress made in robotics and current advancements in AI and big data (Melumad and Pham 2020). Thus, the combination of these technological achievements enables the hospitality industry to implement AI luxury resort apps that provide visitors with semi-customised service experiences (Albayrak et al. 2023).
Numerous upscale resorts have implemented AI service apps that offer guests a range of features to enhance their stay, including self-checkout, spa session reservations, booking sports facilities, reserving tables at hotel restaurants, and securing seats for internal social events, enhancing the perceived value of the whole experience (Chakraborty et al. 2023). The widespread utilisation of AI applications managed by the resorts increases the demand for luxurious amenities in travel entertainment (Buhalis et al. 2019). According to Davenport and Mittal (2023), apps that offer guidance and record consumers experiences are gaining influence at an escalating rate. This delineates the necessity of specific knowledge concerning this massive revolution of AI apps in the business world.
Explaining the intricacies of AI luxury resort apps that offer partially customised service experiences can be challenging in practice, especially considering that the implementation of ChatGPT has expanded the accessibility of AI to a diverse range of individuals (Alto 2023). Combining these data with the findings from global research suggesting that three-quarters of consumers believe that robotics can enhance the total service experience in the tourism sector (Lu, Cai, and Gursoy 2019), we suggest that the rise in popularity of AI apps in the hospitality industry constitutes a new era. Considering the alterations in consumer behaviour resulting from the COVID-19 pandemic, it is now more important than ever to contemplate the incorporation of AI apps within the framework of business strategy (Christofi et al. 2021). This urges the need for a comprehensive model delineating the conceptual pathway that leads to consumers’ behaviour enhancing the perceived value of the total experience.
Previous studies have explored various aspects of AI technology in the hospitality industry. Chi et al. (2023) investigated how trust impacts hospitality services across cultures; Cao et al. (2022) developed a framework explaining guests’ intentions to adopt smart voice assistance technology in Airbnb accommodation; and Choi et al. (2020) explored the effects of human-robot interaction on customer experiences. Additionally, research by Lu, Cai, and Gursoy (2019) focused on consumer satisfaction, while Luo et al. (2020) analysed customer online reviews. To the best of the authors’ knowledge, there has been no study conducted specifically examining consumer behaviour on apps for luxurious AI resorts. The research aims to investigate the use of luxurious AI resort apps as a novel form of ICT from the consumers’ viewpoint, rather than focussing solely on technology users. Consumers and subscribers who can afford luxury hotel experiences and embrace new ICT are recognised for their dual roles as technology enthusiasts and service purchasers. As the maximisation of value is a fundamental and pervasive assumption in the study of consumer behaviour, our cost and benefit analysis through a value adoption model highlights the notion of value, which is defined as the balance between the overall benefits obtained and the overall sacrifices made. The contribution of this study, therefore, lies in investigating the antecedents of consumer behaviour, acceptance, and willingness to purchase luxurious AI resort apps. In particular, this study examines the impact of key factors related to the utilisation of AI-luxurious hotel apps on visitors’ attitudes and willingness to pay for them.
Following this introduction, the next section describes the theoretical background in Section 2. Section 3 describes the proposed theoretical framework and the hypothesis development. Section 4 demonstrates the research methodology, and Section 5 presents the findings of the data analysis. The research concludes in Section 6, with a discussion of the findings, managerial implications, study limitations, and recommendations for future research.
2 Literature Review
2.1 AI Apps Enhancing Tourism
The use of AI-based apps is increasing, with technologies like augmented reality, virtual reality, and mixed reality gaining popularity in the competitive retail industry (Krishen et al. 2021). AI and robotics are also becoming commonplace in the hospitality and tourism sectors, including the accommodation, airline, and restaurant industries (Chui et al. 2018). According to De Carlo et al. (2021), AI apps can be described by the spread of ICT devices and digital technologies that transformed the environment in which both organizations and destinations compete. These AI apps employ a strategic marketing approach that prioritises the needs of buyers and ultimately incentivizes more profitable customer behaviour. Such AI-powered luxury resort apps can also showcase brief videos, which have garnered heightened scholarly interest for their substantial effectiveness in corporate marketing campaigns (Konstantoulaki et al. 2022).
Nowadays, the adoption of AI apps is changing travellers’ experiences, and AI technologies are formulated to improve human-robot interactions (Tussyadiah et al. 2018). Moreover, social media has also transformed tourists from message recipients to co-creators of tourism experiences (Sigala 2016).
Prior research has investigated the use of AI-powered apps by customers for the purpose of managing their daily activities and services (Fernandes and Oliveira 2021). According to these researchers, the functional, social, and relational qualities of a product or service have an impact on consumers’ decisions to adopt it. In their study, Fu, Mouakket, and Sun (2023) discovered that customer trust plays a crucial role in determining their willingness to embrace chatbot technology. The researchers examined the factors that contribute to trust from the customer’s viewpoint, as well as the human-like attributes of chatbots. Cao et al. (2022) contend that AI applications in the hospitality sector can optimally harness user experience values solely when there exists a compelling incentive to embrace the technology being presented. Albayrak et al. (2023) examined the conduct of users of mobile applications while making travel reservations, employing a fusion of the Stimulus Organism Response (SOR) framework and the Technology Acceptance Model (TAM). Their research indicates that the level of excellence of a mobile application has a direct effect on its perceived ease of use and usefulness, which subsequently influences the intention to use mobile apps. Finally, Pillai and Sivathanu (2020) conducted a study investigating the adoption of AI-based chatbots in the hospitality and tourism industries, extending the TAM.
Concerning the AI luxury resort apps, it is found that they are designed to provide intangible services to meet the needs of travellers, with features like providing online reservations, chat support, and recommendations improving the quality of the experience (Li and Stodolska 2021). Examples of resort-specific apps available today include the My Disney Experience app, the Marriott Bonvoy app, and the Hilton Honours app. As a result, travellers can take greater control of organising their stay with an array of information concerning the residence recreational facilities. Under this scope, the adoption of AI apps can create a competitive advantage, but it can also pose risks, such as privacy violations, security issues, and the promotion of fake residences. On the contrary, AI apps can reduce tourists’ stress and enhance memorable destination experiences, derived from a feeling of trust (Kim, Koo, and Chung 2021), since AI algorithms can be adapted for solid financial fraud detection. Regarding the barriers to adopting AI luxurious resorts’ apps, they can assist in the development of robust infrastructure for online apps, enhancing the hospitality sector and providing highly performance services (Huang and Rust 2021).
2.2 The Value-Based Model Approach
The adoption of innovative technologies in the hospitality industry has been analysed from several theoretical angles, such as the technology acceptance model (Davis 1989) in combination with the stimulus organism response model (Jacoby 2002), the unified theory of acceptance and use of technology (Venkatesh and Davis 2000), the value-based theory (Kim, Chan, and Gupta 2007), and the uses and gratification theory model (McLean and Osei-Frimpong 2019). According to Peltier, Dahl, and Schibrowsky (2023), these theories propose that the rate at which technology-driven innovations are adopted depends on the advantages they provide to consumers. Furthermore, Manser Payne, Peltier, and Barger (2021) highlight the ease of achieving these benefits.
These frameworks have been utilised in AI research across multiple domains. Cui, Van Esch, and Jain (2022) examined the impact of AI-powered checkouts on consumers’ purchase intentions. Song and Kim (2022) conducted a study on the impact of human-robot interaction on consumers’ acceptance of humanoid retail service robots. Mostafa and Kasamani (2022) investigated the factors that influence initial trust in chatbots, and a service quality scale was devised for evaluating the performance of artificial intelligence service agents by Noor, Hill, and Troshani (2022). Hasan, Shams, and Rahman (2021) explained the brand loyalty for AI-enabled voice assistants, such as Siri, while Kowalczuk (2018) examined the inclination of consumers to utilise smart speakers, such as Amazon Echo and Google Home. Hollebeek and Belk (2021) clarified consumer behaviour by examining the relationship between their technology usage, brand engagement, and overall well-being. Finally, Baabdullah et al. (2021) examined the factors that contributed to the successful adoption of AI practices by small and medium-sized enterprises.
Despite the extensive use of numerous studies on technology adoption behaviour, such as the widely recognised technology acceptance model mentioned prior, the complex intricacies of human behaviour remain challenging to explain. Thus, there is a significant lack of research studies that have been conducted to investigate the intention to adopt and pay for AI luxury resort apps, mostly considering the perspective of maximising the perceived value. The VAM, proposed by Kim, Chan, and Gupta (2007), is considered the most appropriate option for this study in a voluntary setting when compared to the technology acceptance model. This is because the VAM places a stronger emphasis on functional purpose. Furthermore, in the context of VAM, individuals typically make estimations of perceived values, which encompass all factors related to benefits and sacrifices, in order to determine the worthiness of a technology. Building on the VAM, our model demonstrates higher effectiveness in understanding the tendency to adopt, surpassing previous similar studies. The VAM does not examine individuals who adopt and use traditional technologies as employees in an organisational setting. Instead, it concentrates on individuals who opt to adopt and use new technology for personal reasons, and they are responsible for the voluntary adoption and usage costs themselves (Kim, Chan, and Gupta 2007). Therefore, the VAM exclusively focuses on individuals who adopt new ICT, specifically AI apps, both as users and consumers. The original VAM theory examines customers’ adoption attitudes towards technology from a value-maximising perspective. According to the VAM, consumers’ extrinsic and intrinsic motivations, borrowed from the Cognitive Evaluation Theory (Deci 1971), influence perceived value and behavioural intention (Rogers 2016). Furthermore, the VAM established the value maximisation perspective, suggesting that perceived value is created by comparing benefits with sacrifices. Kim, Chan, and Gupta (2007) suggested the parameters of usefulness and enjoyment as the benefit components of perceived value, while technicality and perceived fee explained the sacrifice components of perceived value. The VAM perspective has been widely adopted by researchers and used in various research contexts such as virtual reality (Vishwakarma, Mukherjee, and Datta 2020), social media usage (Chung and Koo 2015), augmented reality (Lau, Chui, and Au 2019), and IPTV services (Lin et al. 2012). Studies have proved that the VAM is a suitable framework for modelling consumer acceptance (Sohn and Kwon 2020).
Developing this study by advancing the VAM framework, a novel approach to understanding consumers’ acceptance focussing on the attitude of apps owned by luxurious residences is examined. The proposed framework defines the components of the benefits and sacrifices perceived, explaining consumers’ behavioural intentions in accepting and paying to use AI-luxurious apps. The suggested model illustrates the sequential decision-making process of travellers’ acceptance, employing a value-centric approach. It not only examines the direct influence of perceived value on willingness to accept and willingness to pay, but also investigates the mediating role of perceived value in these relationships.
Since this research is constructed by using the strengths of the VAM’s consumer acceptance theory, the usage of AI luxurious resort apps can be connected with factors that positively impact willingness to accept and willingness to purchase attitudes. In this regard, to measure the Benefits, the study borrowed previously examined constructs by the scholars that enhance users’ positive interaction with apps, such as the condition where individuals experience greater pleasure from optional experiential acquisitions, which is Happiness (HAP), according to Van Boven and Gilovich (2003), and Perceived Immersion (PIM), referring to the neurological condition in which an individual is fully focused on an experience evoking emotional resonance (Zak 2022). In the context of this research, established constructs referring to travelers’ Trust (TST), and Change of Habit (COH) toward the use of AI apps, conceptualized as Sacrifices, since negative emotions related to frustration, fear, and anxiousness respectively (Rucker and Petty 2004) may be generated. Moreover, customers evaluate the costs and benefits of their experience through perceived value. Following the VAM’s value maximisation perspective, the proposed model suggests that consumers’ behavioural intentions can be motivated by the experienced value in use (Macdonald, Kleinaltenkamp, and Wilson 2016), which mediates consumers’ behavioural intentions affecting willingness to accept and willingness to pay using AI apps.
3 Conceptual Framework and Hypotheses
The study proposes a research framework based on the VAM (Figure 1), analysing the benefits and the sacrifices through consumer value.
The conceptual model.
3.1 Benefits
Perceived benefits refer to advantages brought by using AI luxurious resort apps. We examine two different motives for adopting the AI luxurious resorts app technology: Happiness and Perceived Immersion.
3.1.1 Happiness
Since HAP is an important sub-dimension of benefits (Venkatesh, Thong, and Xu 2012), there are many studies supporting that HAP has a positive impact on WTA. When individuals experience higher levels of HAP, they appear to be more open-minded and willing to accept new ideas (Knez and Nordhall 2017). According to Hu et al. (2021), inner incentives describe the HAP of using new ICT achievements to provide evidence to foretell consumers’ technology use in the market, while the interactivity between humans and robots in the hospitality industry corresponds to the surroundings of humanistic inner impulses (Lu, Cai, and Gursoy 2019). Since Helliwell, Huang, and Wang (2020) found a positive relationship between HAP and acceptance of change, it is hypothesised that:
H1a.
Happiness has a significant impact on willingness to accept.
H1b.
Happiness has a significant impact on willingness to pay.
H1c.
Happiness has a significant impact on perceived value.
3.1.2 Perceived Immersion
According to Zak (2022), immersion is the neurologic state in which a person is attentive to an experience and it resonates emotionally, and so Zak, explained potential consumer engagement with advertisements by developing the immersion algorithm. Sung et al. (2021) also supported that consumers’ emotional states influence the behavioural intentions of potential consumers. Individuals perceiving high levels of immersion in a virtual environment are more likely to get engaged with novel concepts (Slater and Wilbur 1997). Since more research is needed to establish a determinative relationship, we hypothesize that:
H2a.
Perceived immersion has a positive impact on willingness to accept.
H2b.
Perceived immersion has a positive impact on willingness to pay.
H2c.
Perceived immersion has a positive impact on perceived value.
3.2 Sacrifices
Regarding the impact of factors contributing to the perceived sacrifices of AI luxurious resort apps, we now focus on consumer Trust and the Change of Habit.
3.2.1 Trust
Whereas consumers may suffer a leak of personal data while using AI apps, there is a hidden risk created by the lack of TST which is one of the main reasons why consumers avoid using the new technology (Keen 2000). TST is an essential substance for effective relationships in marketing (Sekhon et al. 2014). According to Wang and Siau (2022), consumers who initially use a new technological product are more likely to continue its use and trust it. Thus, it is hypothesised that:
H3a.
Trust has a positive impact on willingness to accept.
H3b.
Trust has a positive impact on willingness to pay.
H3c.
Trust has a positive impact on perceived value.
3.2.2 Change of Habit
Briefly, based on the theory of Planned Behaviour, human action is guided by three kinds of considerations, which are behavioural, normative, and control beliefs (Ajzen 1991). According to Bamberg, Ajzen, and Schmidt (2003), COH is observed every time there is a change in a behavioural tendency. Habitual use of ICT is an individual’s automatic use of technology based on past learning, while it is measured either as prior behaviour (Kim and Malhotra 2005) or as an alternative where the adoption of perceived technology happens automatically (Limayem and Cheung 2007). According to Lu, Cai, and Gursoy (2019), habit and experience can also predict the use of new AI technological achievements. Since prior knowledge and feedback are both likely to influence future behaviours, according to Ajzen and Fishbein (2005), in this study, habitual factors are considered as customer sacrifices and also predictors of willingness to adopt and pay for AI luxurious resort apps. It is then hypothesised that:
H4a.
Change of habit has a positive impact on willingness to accept.
H4b.
Change of habit has a positive impact on willingness to pay.
H4c.
Change of habit has a positive impact on perceived value.
3.3 Perceived Value
As a starting point for this research, an augmented framework of perceived value is needed while using AI-based luxurious hotel apps based on the VAM. Value in use contributes to goal achievement, symbolising a subjective conceptualization of value (Eggert, Kleinaltenkamp, and Kashyap 2019). Undoubtedly, Alderson and Clewett (1957) and Drucker (1973), among others, established the customer value-based theory of the firm. According to Slater (1997), the main organizational challenge in the customer value-based theory of a company is to maximize the usefulness of the firm’s customer value-creation processes. Several studies also establish that PV positively affects usage acceptance and purchase intention as desirable customer behaviours (Rust and Huang 2012). According to Elshaer and Huang (2023), PV was found to be one of the most important drivers of students’ satisfaction, while Vishwakarma, Mukherjee, and Datta (2020) explained that PV was one of the most important predictors of the adoption of virtual reality. Based on the VAM, the PV is measured when we count the benefits of the sacrifices compared to the final result (Kim, Koo, and Chung 2021). However, it is hypothesized that PV is likely to lead in WTA (H5) and WTP (H6).
H5.
Perceived value has a significant impact on willingness to accept.
H6.
Perceived value has a significant impact on willingness to pay.
Since previous researchers like Sohn and Kwon (2020) tested the VAM they assumed that the PV is a significant predictor of innovative ICT achievements’ adoption. Thus, we hypothesise that the PV mediates the relationship between all indicators and the WTA (H5a–H5d) and that the PV mediates the relationship between all indicators and the WTP (H6a–H6d).
H5a:
Perceived value mediates the relationship between happiness and willingness to accept.
H5b:
Perceived value mediates the relationship between perceived immersion and willingness to accept.
H5c:
Perceived value mediates the relationship between trust and willingness to accept.
H5d:
Perceived value mediates the relationship between change of habits and willingness to accept.
H6a:
Perceived value mediates the relationship between happiness and willingness to pay.
H6b:
Perceived value mediates the relationship between perceived immersion and willingness to pay.
H6c:
Perceived value mediates the relationship between trust and willingness to pay.
H6d:
Perceived value mediates the relationship between change of habits and willingness to pay.
4 Methodology
Based on the literature review, the items and instruments used in the questionnaire to measure the constructs were adapted from previously validated studies to maintain reliability and validity. Thus, we adopted three items to assess HAP developed by Van Boven and Gilovich (2003), four items for PIM (Jennett et al. 2008), five items for TST (Gefen, Karahanna, and Straub 2003), and three items for COH (Lin et al. 2012). We measured PV borrowing from Sirdeshmukh, Singh, and Sabol (2002) three items, four items for WTA adapted from Venkatesh, Thong, and Xu (2012), including two more WTA items from Lu, Cai, and Gursoy (2019), and three items for WTP borrowed from Laroche, Bergeron, and Goutaland (2003). Five-point Likert scales were used to assess all constructs. All the items were slightly modified to suit the AI luxurious resorts app context. We then conducted the pilot study by circulating a survey to 30 travel AI app users via an online questionnaire instrument using Qualtrics (Fraser et al. 2018). To ensure the suitability of the respondents for the research, they were asked four screening questions. Additionally, participants confirmed using AI-powered luxurious residence apps during their most recent vacation within a six-month period. The concluding section enquired about demographic data, level of education, occupation, place of permanent residence, and income. The questionnaire was completed after its final structure was established. We employed the purposive sampling technique to distribute the questionnaire to 600 individuals residing in upscale Greek resorts, selected based on their experiences as outlined in the Appendix (Bussier and Chong 2022). Over the course of two months, we obtained a total of 357 responses. We eliminated 46 of these responses from the data analysis because they were unengaged and contained missing values. The study sample consisted of 311 respondents, with a response rate of 52 %. These respondents were selected from 10 global regions, based on the rigorous criteria established by Kock and Hadaya (2018) and the 10 times rule (Hair et al. 2021).
5 Results
The present research used the Partial Least Squares Structural Equation Modelling technique to analyse the data and the hypotheses proposed in the model through the SmartPLS software version 4.0.9.2 (Hair, Hult, and Ringle 2017). Since common method bias is a crucial issue in behavioural research, we emphasised to the respondents that there was no right or wrong answer included in the questionnaire, declaring that we follow strict confidentiality of the responses. Additionally, we advised all participants to remain neutral and honest while filling out the questionnaire. Harman’s single-factor test was conducted to rule out Common Method Bias. The Variance Extracted using one factor is 11.071 %, less than 50 %, confirming that no factor accounted for the majority of the covariance between the measures, indicating no common method bias in this study (Podsakoff et al. 2003).
The measurement model is designed to measure item reliability, internal consistency of the reliability of the constructs, convergent validity, and discriminant validity (Hair, Hult, and Ringle 2017). We verified the scales’ reliability and convergent validity by employing the normal criteria: item reliability of the measures by using factor loading (>0.7), Cronbach’s alpha and the Composite Reliability (CR) of the constructs (>0.7), and the Average Variance Extracted (AVE) (>0.5). The latent item loadings ranged from 0.816 to 0.873, showing statistical significance. Cronbach’s alpha ranged from 0.808 to 0.908, and CR ranged from 0.886 to 0.929, confirming their reliability. Moreover, AVE ranged from 0.677 to 0.746, above the threshold level of 0.50, which indicates that there is convergent validity to the variables included in the model. To test the discriminant validity, two methods were applied (Tables 1 and 2). The first is the Fornell and Larcker method, and the second is the Heterotrait-Monotrait (HTMT). In the first method, the square root of each latent variable’s AVE is greater than the correlation of its coefficient, indicating discriminant validity in our research (Fornell and Larcker 1981). Henseler, Ringle, and Sarstedt (2014) stated that the HTMT values must be lower than 0.85, which is the case in our study, which also indicates discriminant validity. The values on the diagonal representing the square root of the AVE are: COH = 0.850, HAP = 0.858, PIM = 0.842, PV = 0.863, TST = 0.823, WTA = 0.828, and WTP = 0.859.
Discriminant validity analysis (Fornel & Larcker).
| Constructs | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| 1. COH | 0.850 | ||||||
| 2. HAP | 0.494 | 0.858 | |||||
| 3. PIM | 0.518 | 0.496 | 0.842 | ||||
| 4. PV | 0.441 | 0.471 | 0.437 | 0.863 | |||
| 5. TST | 0.524 | 0.505 | 0.523 | 0.481 | 0.823 | ||
| 6. WTA | 0.485 | 0.493 | 0.471 | 0.543 | 0.529 | 0.828 | |
| 7. WTP | 0.516 | 0.511 | 0.525 | 0.526 | 0.550 | 0.461 | 0.859 |
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Note: Values on the diagonal (italicized) represent the square root of the average variance extracted, while the off diagonals are correlations.
Discriminant validity analysis (HTMT).
| Constructs | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| 1. COH | |||||||
| 2. HAP | 0.603 | ||||||
| 3. PIM | 0.621 | 0.589 | |||||
| 4. PV | 0.533 | 0.570 | 0.515 | ||||
| 5. TST | 0.617 | 0.593 | 0.599 | 0.561 | |||
| 6. WTA | 0.561 | 0.570 | 0.532 | 0.622 | 0.590 | ||
| 7. WTP | 0.626 | 0.621 | 0.624 | 0.633 | 0.644 | 0.530 |
Then the structural model was carried out to test the direct and indirect effects. To test the hypotheses, the statistical bootstrap technique was applied with the recommended 5000 sample size (Ringle, Silva, and De Bido 2014) to obtain the explained variance (R 2), the F 2 effect, and the path coefficients (β), with the observed t values obtained as well as the Q 2 values. In addition, to test the hypotheses that contain mediating variables, the direct and indirect effects of the independent variable on the dependent variable were calculated. The R 2 values represent the explained variance of the dependent variables. PV explains 33.4 % (R 2 = 0.334) of the variability in the data that can be explained by the PV latent variable, while the PV Adjusted R-squared (R 2 Adj = 0.325) takes into account the number of predictors, suggesting that after adjusting for the predictors, approximately 32.5 % of the variation is explained. For WTA, the R 2 value is 0.440, indicating that around 44 % of the variation in the data can be explained by the WTA latent variable. Moreover, the R 2 Adj value of WTA suggests that approximately 43.1 % of the variation is explained after adjusting the predictors. Lastly, since the WTP variable has an R 2 value of 0.469, the WTP latent variable can explain approximately 46.9 % of the variation in the data. The WTP R 2 Adj value of 0.461, accounting for the number of predictors, indicates that around 46.1 % of the variation is explained after adjusting the predictors.
Whereas R 2 measures predictive capacity, Q 2 measures predictive relevance. It is shown that all Q 2 values are positive (Hair, Hult, and Ringle 2017), ranging from 0.243 to 0.340, thus the model has predictive capacity. More specifically, the Q 2 value of 0.243 suggests a moderate level of predictive ability for the PV variable. The Q 2 value of 0.297 suggests a moderate level of predictive ability for the WTA variable. The Q 2 value of 0.340 suggests a relatively higher level of predictive ability for the WTP variable compared to the other two variables. The F 2 values indicate the magnitude of the relationships between the variables. Generally, the F 2 values across the relationships range from 0.012 to 0.090, which suggests small effect sizes. This means that the relationships between the variables have a relatively modest impact or influence on each other. For instance, the relationships between COH and Latent Variable PV, Latent Variable WTA, and Latent Variable WTP all have small effect sizes with F 2 values of 0.019, 0.019, and 0.026, respectively. Similarly, the relationships involving HAP, PIM, and TST also exhibit small effect sizes.
The hypotheses H1a–H1c as shown in Table 3 are being supported since the standardised β regression weights are significant and positive (β = 0.144, 0.144, and 0.219, i.e. WTA, WTP, and PV), which means that the inner impulse of HAP appears to be one of the most significant factors of AI technology acceptance (Venkatesh, Thong, and Xu 2012). The results outlined that the hypotheses H2a-H2c, named PIM (β = 0.107, 0.174, and 0.137), directly enhance customers’ adoption of AI apps PV and WTP, suggesting that tourists are likely to be effectively committed when experiencing perceived immersion. The results support H3a, H3b, and H3c, as TST positively impacts WTA, WTP, and PV, supporting standardised β regression weights equal to β = 0.198, 0.202, and 0.223, respectively. In addition, the support for H4a, H4b, and H4c indicates that COH has a positive and significant impact on WTA, WTP, and PV, since new habits versus experience may predict the acceptance of using and purchasing new AI apps. The results outlined that both hypotheses H5 and H6 named PV (β = 0.274) and (β = 0.218) directly lead to WTA and WTP attitudes, evaluating acceptance of AI luxury resort app use (Gursoy et al. 2019).
Hypotheses testing direct effects.
| Hypothesis | Direct relationships | Std. beta | Std. error | T values | p values |
|---|---|---|---|---|---|
| H1a | HAP → WTA | 0.144 | 0.048 | 3.022 | ** |
| H1b | HAP → WTP | 0.144 | 0.062 | 2.312 | * |
| H1c | HAP → PV | 0.219 | 0.051 | 4.297 | *** |
| H2a | PIM → WTA | 0.107 | 0.045 | 2.346 | * |
| H2b | PIM → WTP | 0.174 | 0.043 | 4.062 | *** |
| H2c | PIM → PV | 0.137 | 0.056 | 2.432 | * |
| H3a | TST → WTA | 0.198 | 0.043 | 4.603 | *** |
| H3b | TST → WTP | 0.202 | 0.048 | 4.179 | *** |
| H3c | TST → PV | 0.223 | 0.058 | 3.876 | *** |
| H4a | COH → WTA | 0.134 | 0.050 | 2.699 | ** |
| H4b | COH → WTP | 0.153 | 0.049 | 3.109 | ** |
| H4c | COH → PV | 0.145 | 0.053 | 2.747 | ** |
| H5 | PV → WTA | 0.274 | 0.071 | 3.863 | *** |
| H6 | PV → WTP | 0.218 | 0.045 | 4.879 | *** |
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Note: *Indicates significant paths: *p < 0.05, **p < 0.01, ***p < 0.001, NS = not significant.
Concerning the influence of the mediator variable of PV on WTA and WTP performance, consumer behaviour based on PV has a positive and significant influence on adopting AI apps, estimated as HAP- > PV- > WTA (β = 0.06), PIM- > PV- > WTA (β = 0.038), TST- > PV- > WTA (β = 0.061), and COH- > PV- > WTA (β = 0.040). The mediation effect results, supporting H5a–H5d, are shown in Table 4. Admittedly, when consumers obtain PV, they are feeling satisfaction and willingness to adopt and pay using those tested AI luxurious resort apps (Doss 2015). Additionally, the results are similar for H6a, H6b, and H6d since PV mediates the relationship between the estimated HAP- > PV- > WTP (β = 0.048), PIM- > PV- > WTP (β = 0.030), TST- > PV- > WTP (β = 0.049), and COH- > PV- > WTP (β = 0.032), showing that the PV a tourist obtains may enhance the willingness to purchase a luxurious resort AI app. Figure 2 shows the structural model of the research.
Hypotheses testing mediation effect.
| Hypothesis | Indirect effects | Std. beta | Std. error | T values | p values |
|---|---|---|---|---|---|
| H5a | HAP → PV → WTA | 0.06 | 0.020 | 3.031 | ** |
| H5b | PIM → PV → WTA | 0.038 | 0.018 | 2.090 | * |
| H5c | TST → PV → WTA | 0.061 | 0.023 | 2.663 | ** |
| H5d | COH → PV → WTA | 0.040 | 0.019 | 2.094 | * |
| H6a | HAP → PV → WTP | 0.048 | 0.015 | 3.126 | ** |
| H6b | PIM → PV → WTP | 0.030 | 0.015 | 1.994 | * |
| H6c | TST → PV → WTP | 0.049 | 0.016 | 3.060 | ** |
| H6d | COH → PV → WTP | 0.032 | 0.013 | 2.343 | * |
-
Note: *Indicates significant paths: *p < 0.05, **p < 0.01, ***p < 0.001, NS = not significant.
Graphical representation of the structural model.
6 Discussion and Conclusion
The primary objective of this research is to investigate the factors that influence guests’ tendency to adopt and purchase AI-based luxury residence apps, with a particular emphasis on maximising the perceived value. Evaluating consumer choice and decision-making through the lenses of economics and marketing research, we measure the components of benefits and sacrifices, following the value maximisation perspective, which mediates the behavioural intentions that affect willingness to accept and willingness to pay using AI luxurious resorts’ apps. Overall, our research strongly supports the claim that factors such as happiness, perceived immersion, trust, and change of habits have a positive impact on consumers’ perceptions of value acceptance when it comes to paying for the use of AI-luxurious apps.
This study utilised the value-based theory, resulting in the finding that the adoption intention of AI-luxurious resorts’ apps is significantly influenced by perceived value. The study’s findings align with prior research, which suggests that perceived value plays a crucial role in the adoption of new ICT. The works of Chen, Hsiao, and Wu (2018), Hsu and Lin (2018), and Kim, Chan, and Gupta (2007) support this conclusion. Highlighting the significant influence of perceived value on the acceptance of AI-based apps created by high-end resorts, the perceived advantages have not shown a different impact on the perceived value compared to the perceived disadvantages. The dimensions of trust and happiness were determined to be more substantial and exert a favourable influence on perceived value. The aforementioned discovery is consistent with the study by Hirunyawipada and Paswan (2006) on the acceptance of cutting-edge technological products. They also correspond with previous studies, suggesting that the innate longing for happiness serves as the primary motivation for embracing AI technology (Cao et al. 2022; Hollebeek and Belk 2021; Song and Kim 2022). When tourists feel fully immersed in their experiences, they are more likely to commit to them. It is evident that individuals can achieve greater happiness by prioritising the acquisition of experiences over material possessions (Kahneman 1999). Similarly, the tourism industry will cultivate greater satisfaction among their customers by offering a plethora of opportunities for acquisition (Van Boven and Gilovich 2003). Comparing the perceived sacrifice of changing habits with the potential rewards of adopting new habits can help predict whether people will use and buy new AI apps. However, research shows that this has little impact on the perceived value of using AI luxury resort apps. One possible explanation for this is that users are already highly familiar with using social AI apps. The findings also suggest that engaging in sensation-seeking behaviour positively influences the intention to adopt AI luxurious resort’s apps, aligning with the recent study on the adoption of virtual reality in tourism destinations by Vishwakarma, Mukherjee, and Datta (2020). The outcome can be attributed to consumers’ perceptions, as they found the utilisation of AI luxury resort apps to be distinctive and thrilling.
It is noteworthy to mention that the mediating role of perceived value provides empirical evidence that supports increased sales and business success. This further strengthens the argument that institutions have a direct impact on customers’ assessments of value (Edvardsson et al. 2014). Following the second process of customer value creation, the customer order is being fulfilled, while consumers are using the new AI app for the first time (Day 1994). During the final stage of creating value for the new AI app, it is important to consider market trends when rating its potential success. By integrating the most relevant findings of the ICT technology adoption and value literature, the proposed and empirically tested model of AI luxurious residence apps represents a novel approach to understanding consumers’ acceptance and willingness to buy AI apps. Since we are in marketing, we should be committed to the premise that the creation of customer value must be the reason for the company’s existence and, certainly, for its success (Slater and Wilbur 1997).
In conclusion, the results of our research also align with other studies, indicating that the likelihood of adopting and acquiring new AI applications can be anticipated by comparing recent behaviours with previous experiences, resulting in a favourable and noteworthy influence on the inclination to embrace, the willingness to purchase, and the perceived worth (Cui, Van Esch, and Jain 2022). The more perceived value the consumers receive, the more willing they are to purchase AI apps for their luxurious resort stay (Huang and Rust 2021). However, the relationships investigated are by no means exhaustive. A comprehensive analysis of the managerial implications and suggestions for future research is thoroughly presented.
6.1 Managerial Implications
As customer-perceived value is a driver in the marketing concept (Kleinaltenkamp et al. 2022), it is suggested that its mediating role derived from the proposed model should alert profit firms, hotel directors, and practitioners in the tourism industry to observe customer acceptance behaviour when dealing with the adoption of ICT innovations. The study highlights the importance of balancing perceived benefits and sacrifices when adopting AI apps for luxury hotels, suggesting that tour marketers should focus on improving these benefits and minimising drawbacks. They should promote these AI apps at popular tourist exhibitions and emphasise their efficiency in obtaining tourism-related information. No app can win the minds and hearts of consumers unless the engaged hospitality sector promises and internalises what it stands for. As such, luxurious resort apps wishing to improve their customers’ acceptance need to carefully adopt an orientation to ensure that they create feelings of enjoyment, confidence, and immersion for customers. Since in hospitality surroundings, optimising customer experience will continue to require technological inspiration melted into various aspects of services, it is a fact that tourism services thrive on providing interpersonal interactions to create customer value (Lu, Cai, and Gursoy 2019). To increase trustworthiness, marketers should use online launch campaigns, TV promotions, and influencer endorsements (e.g. TikTok, Instagram, and YouTube channels). Furthermore, our results corroborate studies emphasising the necessity of introducing the use of AI in service marketing, acknowledging consumers emotional interactions and perceptions, fulfilling consumer needs, having electronic word-of-mouth awareness, ameliorating merchandise achievements, utilising AI in branding and strategic marketing, changing perspectives in consumers’ whole experience, and moving one step forward in the science of marketing (Mustak et al. 2021). This evolution enhances the worldwide adoption of AI applications. Hotel managers must carefully supervise and integrate the relevant elements into their operations, ensuring they are in line with their marketing objectives, expected results, and target customers.
The findings also have implications for assisting ICT developers in comprehending the primary factors that drive customer acceptance behaviour. In order to expand their customer base, they can do this by improving their AI app offerings, increasing profitability, and putting effective marketing strategies in place. Likewise, non-profit organisations in the tourism and hospitality industries can also gain advantages from comprehending the emotional factors that impact tourists’ behaviour. These insights can assist specific stakeholders in enhancing their services and more effectively meeting the preferences and requirements of tourists. Since the study suggests targeting tourists seeking unique experiences using AI apps, online travel agencies can promote these applications through visual content. Lastly, governmental agencies such as the Greek Ministry of Tourism and policymakers in the tourism industry can enhance destination promotion and benefit from this research by creating guidelines and incentives to encourage the use of AI apps in the hospitality sector. This will provide valuable information to decision-makers and enable them to establish ethical regulations regarding the acceptance of AI technology in the hospitality industry.
6.2 Limitations and Future Research
The current research has several limitations since it was carried out in Greece, aligning with the usual profile of tourists who visited Greece during the period of March to April 2023. According to Eurostat (2023), Greece had the highest percentage of employment in the tourism industry in 2020, contributing around 24 percent of the country’s total non-financial business economy. In both the Hellenic Republic (2023) and SETE, tourism represents 18 % of Greece’s gross domestic product. Hence, it is vital to give priority to the evaluation of the hotel industry, which constitutes 10 % of the non-financial business sector in the European Union. According to Eurostat (2020), this industry is tackling unemployment by employing a workforce of 10.9 million individuals, thereby promoting economic growth (Zurub, Ionescu, and Constantin 2015).
Future studies focussing on specific sectors or industries would help generalise the strategic importance of AI-enabled technology, which has metamorphosed the retail perspective by enriching customer-company interaction through reality-enhancing online interfaces (Kaplan and Haenlein 2020). The advancements in artificially intelligent technologies are expected to generate additional value by creating new business opportunities and enhancing the quality of life for consumers (Durmaz and Baser 2023). Additionally, further research should assess this orientation along with other important marketing constructs, taking into account that experienced value in use is a dynamic concept that changes over time (Kleinaltenkamp et al. 2022). Future research could focus on other types of AI apps, investigating the impact of other antecedents of the willingness to adopt the use of AI apps and the willingness to purchase AI apps that can add value to the current framework and increase the model’s predictive power.
It is important to note that although the industry’s adoption of AI remains significantly low, it is mainly limited to specific initiatives, mostly undertaken by large corporations, due to the novelty of this technology. Therefore, there is a significant opportunity to carry out ICT research in the area of smart tourism, which companies can utilise to protect their advantages in their operational environment. Thus, a notable constraint in this field of study is the inability of scholars to employ extensive surveys in the industry for the purpose of validating different theories and frameworks (Stroumpoulis, Kopanaki, and Varelas 2022).
Today, we are living in an era where artificial intelligence holds great potential in the fields of marketing research, strategic marketing, and marketing operations. AI apps are crucial for businesses as they allow for the implementation of personalised campaigns in real-time, resulting in a positive return on investment and an enhanced consumer experience (Chintalapati and Pandey 2022). Finally, it is anticipated that the utilisation of AI apps, capable of effectively identifying consumers and enticing potential customers through the analysis of large datasets, will result in the enhancement of consumer sentiments and marketing effectiveness (Doborjeh et al. 2022).
Appendix A: Questionnaire Scales
| Constructs | Item no. | Items | Reference |
|---|---|---|---|
| Happiness | HAP1 | When you think of your interaction with the AI application of the luxurious residence you are staying, how happy does it make you feel? | Van Boven and Gilovich (2003) |
| HAP2 | How much does your interaction with the AI application of the luxurious residence you are staying, contribute to your happiness? | Van Boven and Gilovich (2003) | |
| HAP3 | To what extent do you think the money spent to use the AI application of the luxurious residence you are staying, is worth it? | Van Boven and Gilovich (2003) | |
| Perceived immersion | PIM1 | During my stay once using the AI application of the luxurious residence, I was unaware of what was happening around me. | Jennett et al. (2008) |
| PIM2 | During my stay once using the AI application of the luxurious residence, I felt disconnected from the outside world. | Jennett et al. (2008) | |
| PIM3 | During my reservation I felt that I was actually travelling when using the AI application of the luxurious residence I was at about to stay. | Jennett et al. (2008) | |
| PIM4 | During my stay while using the AI application of the luxurious residence, I feel is in another world. | Jennett et al. (2008) | |
| Trust | T1 | I would trust the AI application of the luxurious residence to perform the expected task without any errors. | Gefen, Karahanna, and Straub (2003) |
| T2 | I believe that the AI application of the luxurious residence performance would be trustworthy. | Gefen, Karahanna, and Straub (2003) | |
| T3 | I would trust the performance of the luxurious residence’s AI application, until it gives me a reason not to. | Gefen, Karahanna, and Straub (2003) | |
| T4 | My tendency to trust the performance of the luxurious residence’s AI application would be high. | Gefen, Karahanna, and Straub (2003) | |
| T5 | I would feel confident using the AI application of the luxurious residence. | Gefen, Karahanna, and Straub (2003) | |
| Change of habits | CH1 | I think the information found in AI application of the luxurious residence cannot be found elsewhere. | Lin et al. (2012) |
| CH2 | I think the information found in AI application of the luxurious residence is different from the ones on the web. | Lin et al. (2012) | |
| CH3 | I think the services provided by the AI application of the luxurious residence are quite different from the ones on the web. | Lin et al. (2012) | |
| Perceived value | PV1 | Compared to the potential fee of the AI application of the luxurious residence I need to pay during my visit, it offers better value in planning services. | Sirdeshmukh Singh, and Sabol (2002) |
| PV2 | Organizing and planning my stay compared to the time I need to spend the use of the luxurious residence’s AI application is worthwhile to me. | Sirdeshmukh Singh, and Sabol (2002) | |
| PV3 | Overall, the use of the AI application of the luxurious residence in planning delivers better value to me. | Sirdeshmukh Singh, and Sabol (2002) | |
| Willingness to accept the use of AI residence apps | WTA1 | I am willing to download the AI application of a luxurious residence. | Venkatesh, Thong, and Xu (2012) |
| WTA2 | I will feel happy to interact with the AI application of a luxurious residence. | Venkatesh, Thong, and Xu (2012) | |
| WTA3 | I am likely to interact with the AI application of a luxurious residence. | Venkatesh, Thong, and Xu (2012) | |
| WTA4 | I plan to use the AI application of a luxurious residence in the next 6 months. | Venkatesh, Thong, and Xu (2012) | |
| WTA5 | I intend to use the AI application of a luxurious residence in the next 6 months. | Venkatesh, Thong, and Xu (2012) | |
| WTA6 | I predict I would use the AI application of a luxurious residence in the next 6 months. | Venkatesh, Thong, and Xu (2012) | |
| Willingness to pay the use of AI residence apps | WTP1 | It is acceptable to pay for buying subscription to use personalized luxurious residences’ AI applications. | Laroche, Bergeron, and Goutaland (2003) |
| WTP2 | I feel proud to have personalized AI applications of luxurious residences on my mobile phone though they are payable. | Laroche, Bergeron, and Goutaland (2003) | |
| WTP3 | I would be willing to spend an extra 3 % per stay in order to buy personalized AI applications of luxurious residences. | Laroche, Bergeron, and Goutaland (2003) | |
|
|
|||
| Demographics | |||
|
|
|||
| Gender | What is your gender? | ||
| Female = 53 % | |||
| Male = 47 % | |||
| Age | What is your approximate age? | ||
| 20–30 = 2 % | |||
| 31–40 = 11 % | |||
| 41–50 = 56 % | |||
| 51–60 = 29 % | |||
| 61–70 = 1 % | |||
| Over 70 years = 1 % | |||
| Purpose of stay | What is the purpose of your stay? | ||
| Family vacation including children = 48 % | |||
| Family vacation without children = 33 % | |||
| Couple vacation = 12 % | |||
| Other = 7 % | |||
| Place of the luxurious resort: | Where did you stay? | ||
| Attica = 15 % | |||
| Macedonia and Thrace = 6 % | |||
| Epirus and Western Macedonia = 2 % | |||
| Thessaly and Central Greece = 2 % | |||
| Peloponnese, Western Greece and the Ionian = 10 % | |||
| Islands of the Aegean = 27 % | |||
| Crete = 38 % | |||
| Spain = 3 % | |||
| Germany = 11 % | |||
| France = 8 % | |||
| Turkey = 4 % | |||
| Israel = 18 % | |||
| U.K. = 19 % | |||
| U.S.A. = 22 % | |||
| Other = 2 % | |||
| Place of permanent residence: | Where do you live? | ||
| Greece = 7 % | |||
| Italy = 6 % | |||
| Education: | What is your educational level? | ||
| High School = 2 % | |||
| Vocational studies = 3 % | |||
| University = 45 % | |||
| Master’s = 37 % | |||
| Ph.D. = 13 % | |||
| Income: | What is your monthly income range in euros? | ||
| Less than 1.000 euros = 0 % | |||
| 1.001–1.800 euros = 4 % | |||
| 1.801–2.500 euros = 20 % | |||
| 2.501–3.200 euros = 23 % | |||
| More than 3.200 euros = 46 % | |||
| Prefer not to answer = 7 % | |||
| *Screening questions | *I have downloaded the AI application of the luxurious residence I am staying | YES = 100 % | |
| NO | |||
| *Favorite ICT device | Mobile phone = 69 % | ||
| Tablet = 8 % | |||
| PC = 16 % | |||
| Smartwatch = 7 % | |||
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Artikel in diesem Heft
- Frontmatter
- Reduction of Ivory Product Purchase in China: The Role of Cultural Values on Ethical Consumption
- The Prospection and Retrospection of Experiential Purchases as More Meaningful Memories: Social and Affective Implications
- “How Brands Can Influence Consumers’ Attitudes Towards Visiting Brand-related destinations”
- Consumer Behaviour on AI Applications for Services: Measuring the Impact of Value-Based Adoption Model on Luxurious AI Resorts’ Applications
- AI-Powered Augmented Reality App Satisfies My Beauty Needs and Want
- The 4Ps Marketing Strategy as a Driver of Dynamic Capabilities: Path to Consumer/Voter Satisfaction
- Authenticity as a Strategic Weapon: Navigating the Social Media Battlefield to Enhance Brand Loyalty
- Allurement of Augmented Reality on Behavioral Intention: Delineating the Role of Visual Appeal and Arousal Using Information System Success Model
- Don’t Tell Me to Have, but to Do It! The Role of Luxury Desirability Motives in Reducing Counterfeiting Schadenfreude
- “Gusto” or “Taste”? Anglicisms Change Perceived Product Risk and Product Appeal in Italian Print Advertising
- Does Advertising Facilitate Supplier-Provided Trade Credit?
- Understanding the Incongruent Brand Personalities on Social Media: Evidence from Indian Brands
Artikel in diesem Heft
- Frontmatter
- Reduction of Ivory Product Purchase in China: The Role of Cultural Values on Ethical Consumption
- The Prospection and Retrospection of Experiential Purchases as More Meaningful Memories: Social and Affective Implications
- “How Brands Can Influence Consumers’ Attitudes Towards Visiting Brand-related destinations”
- Consumer Behaviour on AI Applications for Services: Measuring the Impact of Value-Based Adoption Model on Luxurious AI Resorts’ Applications
- AI-Powered Augmented Reality App Satisfies My Beauty Needs and Want
- The 4Ps Marketing Strategy as a Driver of Dynamic Capabilities: Path to Consumer/Voter Satisfaction
- Authenticity as a Strategic Weapon: Navigating the Social Media Battlefield to Enhance Brand Loyalty
- Allurement of Augmented Reality on Behavioral Intention: Delineating the Role of Visual Appeal and Arousal Using Information System Success Model
- Don’t Tell Me to Have, but to Do It! The Role of Luxury Desirability Motives in Reducing Counterfeiting Schadenfreude
- “Gusto” or “Taste”? Anglicisms Change Perceived Product Risk and Product Appeal in Italian Print Advertising
- Does Advertising Facilitate Supplier-Provided Trade Credit?
- Understanding the Incongruent Brand Personalities on Social Media: Evidence from Indian Brands
