Sage Journals: Discover world-class research

Abstract

The main objective of this article is to investigate how the AI interactive approaches of travel apps affect tourists’ travel choices directly or indirectly through perceived images, in order to further understand whether tourists can have a favorable user experience in the face of this non-human service, and whether the interactive approaches should be adapted to satisfy the marketing intentions set by the travel app in response to the competitive strength in the marketplace. The research methodology uses quantitative analysis, hypotheses supported by a theoretical background and the formation of a research model, the collection of basic data through questionnaires, and data analysis using structural equation modeling with Amos 22.0. The major findings are associated with the essential role of the interactive approaches (including informativeness, playfulness, and personalization approaches) offered by the travel apps on both the users’ perceived images and travel choices. This not only emphasizes the role of AI interactive approaches in the experiential travel assistance provided to users, but also represents a stronger marketing element to the destination perceived image composition and travel decisions of tourists. The findings also support the development of approaches for human-android interaction and tourism marketing with AI as the main service. This research has the scientific value of an integrated disciplinary analysis that combines theories from computer technology, tourism management, and psychology.

Keywords

artificial intelligence travel choices travel apps perceived images

Introduction

As the audience groups for travel apps continues to proliferate due to advances in mobile technology, their ever-growing and changing needs are causing human customer service to become overwhelmed. This has led some mobile programs to invest in and use interactive services based on artificial intelligence (AI) technology in the expectation of being able to respond to users’ needs speedily. In reality, the widespread use of AI technology has already brought some convenience to the development of the tourism industry, especially in terms of potential tourists’ access to information on destinations (Grundner & Neuhofer, 2021).

In fact, it is not just limited to the tourism sector, as the entire tertiary industry is rethinking the approach to planning product marketing and service enhancement due to advances in AI web technology and the increasing reliance on mobile social networking systems (Wirtzet al., 2021). Artificial intelligence acts as a novel service role, offering tourism and hospitality industry the possibility to customize their tourism and hospitality products beyond the traditional service perspectives (Gkikas & TheodoridisK, 2019). For example, since 2014, international hotels such as Marriott and Starwood Aloha have been using a variety of AI intelligent robots to provide customers with multiple services such as booking, check-in, answering and cleaning (Cain et al., 2019). Companies such as Google Travel, Tata Consultancy Services and Travel Advisors have done several studies on the use of AI in the travel and hospitality industry and have found that at least 85% of service providers utilize AI for services in their travel and hospitality business (Samala et al., 2022).

In the application area of travel apps, AI technologies mainly provide interactive service technologies such as pan-question and answer systems, and task or goal-based dialogue systems (Choi et al., 2019). Pan-question and answer systems, which aim to find precise information from structured (e.g., knowledge bases, tables) and unstructured (e.g., documents) answers to user questions, are single-round dialogue systems (Wenskovitch & North, 2020). Task or goal-based dialogue systems, on the other hand, require interaction to achieve a specific task or goal, such as various intelligent assistants, ticket booking and food ordering systems (Ferreira & Lefèvre, 2015).

In particular, interactive artificial intelligence provides more powerful information data processing capabilities that can be utilized in the online social environment of travel app users to assist them in optimizing their choice of routes, hotels and travel options for potential tourism activities. In fact, it is becoming more common for people to plan travel activities, purchase travel products and consume travel services through mobile social networks, and the interactive capabilities of artificial intelligence are further enhancing the wisdom of this approach (Koo et al., 2021; Lalicic & Weismayer, 2021; Leung, 2019). Tourists can quickly gather knowledge about destinations through voice questions to the AI, thus reducing confusion in making travel plans due to tedious details (A. Huang et al., 2022). On the other hand, the powerful destination information processing capabilities in the AI database enable a targeted marketing image of the destination to be delivered more effectively to tourists, enabling them to get a better impression of the desired destination before they travel (Ceylan et al., 2021). For example, the artificial intelligence customer service of Qunar, a travel app from China, can analyze and identify users’ browsing history to predict their travel preferences, thereby pushing relevant destination marketing information to relevant customers in advance of China’s golden weeks, in order to enhance the destination’s marketing expectations and users’ sense of experience (Xu et al., 2019). This rapid information sharing process brings travel app users, destinations, and travel marketing tools closer together, while AI interactive technology plays the role of information leader in cross-market sharing.

Hence, because of the powerful tools of AI technology such as intelligent interaction, information recall, data recording, analysis and prediction, and fast retrieval, travel apps can be further enriched by this technology to deepen the interactive connection with users and further enrich their travel service capabilities (M. H. Huang & Rust, 2021; Ivanov & Webster, 2019; Jiao & Chen, 2019; L. Li et al., 2021; VanNuenen & Scarles, 2021).

Given the vast promise of AI technology for the travel app business, some local travel apps are also committed to developing applications in this area in order to improve their market competitiveness. One of the key issues is to study and address the logic of the application of AI interactive approaches (Rzepka & Berger, 2018; Sundar, 2020). This can further help to understand the key needs of users in using AI smart assistance and how this technology influences their travel choices. Furthermore, tourists’ travel choices are often governed by their perceived image of the desired destination. Information familiarity, on the other hand, is a central factor influencing the perceived image of the tourist’s constituent destinations (Baloglu & McCleary, 1999; Santana & Sevilha Gosling, 2018; Wang et al., 2020). It is also worth exploring the academic topic of how artificial intelligence interactive approaches can help tourists improve their access to information and thus influence their perceived image of the destination.

Existing research has focused more extensively on user acceptance of AI technologies in the tourism industry (Jabeen et al., 2022). These include both the experience of the service providers and the tourism consumers (Tussyadiah, 2020). Among them, the ease of use and usefulness of the technology are further proven to be the key antecedents of the shifting attitude of travel users toward AI technology (Parvez et al., 2022; Phaosathianphan & Leelasantitham, 2019). However, the technology acceptance framework mainly attempts to provide information aids for the management of the organization from a holistic perspective, with very little further sorting out of the relationship between specific modules and marketing. On the other hand, even though the important role of perceived image in tourism destination marketing is aware (Pike & Page, 2014), the theoretical combination of perceived image and AI interaction technology is still rare. This study then attempts to further investigate these gaps.

The main objective of this paper is to design and construct a model for travel app users to perceive destination images through AI interactions and further generate travel choices, using the perceived image formed by the user’s perceived destination information as a mediating variable to analyze the impact of AI interactive technology on the travel choices made by mobile users. In this study, a hypothetical deductive research method is used to predetermine the logical dimensions of each variable with theoretical foundations from previous literature and to formulate hypotheses about the influence of interrelationships. The underlying data relied on questionnaire collection. The results of the data analysis are finally obtained further and summarized depending on structural equation modeling analysis.

In particular, the first part of this article analyses significant evidence on the impact of AI interactive approaches and perceived images on the travel choices of mobile travel users, while the second part seeks to examine their intention to use AI intelligent interactive approaches to support their travel decisions through a quantitative analysis of a questionnaire involving 831 respondents. Respondents were all from China, a restriction that could firstly target the needs of the Chinese travel app market more positively; furthermore, as the civil development levels of AI technology applications are gradually converging globally, Chinese travel apps are roughly the same as mainstream foreign travel apps in terms of interaction approaches. This contributes to the academic extension of this study. On the other hand, the source of the sample is not limited to one brand of travel app, but the six dominant Chinese mainstream travel apps are selected, which would reduce the respondents’ bias. This research has an integrated disciplinary value in practice, linking theories from tourism management, computer technology and psychological discipline.

Theoretical Background

Dimensions of AI Interactive Approaches

Artificial intelligence technology is one of the new interactive tools used by the information technology intelligent service platform to realize informational services for customers. At present, it is mainly used to provide users with information-based responses based on a large amount of data (Liao et al., 2020). In the field of travel app applications, AI customer service can provide mobile users with services such as itinerary planning, travel information, hotel booking, traffic enquiries, sharing of popular attractions, travel tips and more (Cheriyan et al., 2022; Dey & Shukla, 2020; Jarrar et al., 2020; L. Zhang & Sun, 2021).

In some of the early 21st century AI application ideas, interaction is considered to be the basis for their front-end operation use (Allen et al., 2001; García-Serrano et al., 2004; González et al., 2004). Wiener (1948) was the first to define interaction in terms of information dissemination. He defined interaction as a mechanism for disseminating information, two-way communication and feedback between the receiver of information and the source. Ha and James (1998) described interaction as the degree of two-way communication and mutual responsiveness between the communicating parties. Artificial intelligence interaction is a type of human-computer interaction that reflects the interaction between humans and AI devices or technologies (Miller, 2019).

AI interactive approaches are the technical thinking that dominates AI service delivery and is a fundamental prerequisite for improving the experience for users (Jiang et al., 2022). Among them, informative interaction is widely used in tourism-related intelligent service systems or online services. (Ho & See-To, 2018; Sudapet et al., 2021; Tussyadiah, 2020). The main reason for mobile users to access information through mobile apps is the ease and breadth of access to information (C. D. Huang et al., 2017; Y. Zhang et al., 2021). Travel app users care about fast response to information acquisition, whereas AI customer service provides a quick question and answer solution that is both intelligent and user-friendly (Hoang & Phan, 2021). Therefore, this strategy has been used as a common dimension for analysis in AI human-computer interaction research (Jarrar et al., 2020).

Playfulness is another AI interactive approach that has been utilized frequently (Mirra & Pugnale, 2022). It refers to the extent to which the system offers features that reflect fashion trends and entertain and delight consumers (Byun et al., 2017; K. Chen & Yen, 2004). This often tests the AI’s ability to gauge travel trends and inform relevant users with sensible advertising (Samala et al., 2022; W. Zhang et al., 2019). For example, the World Cup, the Olympic Games and other popular events are held by the AI customer service to push the nature of the popular travel items with discounts. For freelance tourists, the pleasure of pre-travel often comes from the careful preparation of their trip (De Gruyter et al., 2017), while playfulness interactions can improve the AI’s ability to act as a plan-making assistant.

Personalization is the dimension considered in terms of personalized service for users (C. D. Huang et al., 2017; Westerman et al., 2020). The primary role that AI takes in travel apps is customer service, which is inevitably governed by the logic of Customer Relationship Management (CRM) applications. In a CRM system, recording and identifying information about users and using this to analyze their online behavior is an important basis for providing them with a personalized marketing service (Askool & Nakata, 2011; Chatterjee et al., 2019). The users’ satisfaction derives from the fact that AI’s personalized service matches their expectations of use.

In summary, this article uses informativeness, playfulness, and personalization as the main dimensions of the AI interactive approaches.

Theoretical Linkages to Travel Choices

Travel choice is a condition in which tourists react psychologically to tourism products and services (Beritelli et al., 2019; Heggie, 1978). It is the decision-making process by which tourists find, buy, use, evaluate and dispose of tourism products and services (Wong et al., 2016). As AI technologies are widely used to support users’ decision-making, exploring the relevance of AI-based interactive behaviors to users’ travel choices is gaining traction. In essence, AI systems are algorithm-based computer science devices as any other data system, but in contrast, AI systems have deep learning capabilities and provide more accurate information and personalized recommendations (Schneider & Leyer, 2019). AI utilizes interactive approaches such as informativeness to quickly respond and characterize information, which can effectively enhance the level of information available to users when making decisions, thus increasing their willingness to make choices using AI recommendations (J. Kim et al., 2021). Further, providing personalized advice to tourists is an important task for AI systems, whereas personalization approaches drive the AI to use learning and analytic capabilities to master users’ preferences and match their selection expectations (Arentze, 2013; Chatterjee et al., 2019).

On the other hand, playfulness creates a good emotional connection when AI supports users in making choices, and it reflects the intuitive feelings that AI customer service brings to the user during the communication process (J. Kim et al., 2022), including visual and aural enjoyments. For instance, advances in voice technology have made AI interaction more deeply interesting (Balakrishnan et al., 2021). Some users usually change the AI voice to their favorite celebrity to increase the pleasure between human-AI conversation. Playfulness has created a degree of user attachment to AI among travel app users, making them more trusting of AI-assisted decision-making (J. Kim et al., 2022; Miller, 2019; Tussyadiah, 2020).

In fact, the ability of artificial intelligence to assist is applied to almost all stages of travel app users making travel choices (Y. Zhang et al., 2021). While interactive approaches are important tools to drive effective impact on AI’s customer service. Thus, this study hypothesizes the following relationships:

H1. AI interactive approaches for travel apps have direct influences on users’ travel choices.

H1a. Interactive approach of informativeness is directly influencing users’ travel choices.

H1b. Interactive approach of playfulness is directly influencing users’ travel choices.

H1c. Interactive approach of personalization is directly influencing users’ travel choices.

Theoretical Linkages to Perceived Images

Perceived image is considered to be a mental construct or state representation of a tourist’s perception on a destination (Agapito et al., 2013; Gartner, 1994). In general, the tourist’s perceived image of a destination is influenced by a mixture of conditions, one important factor being informational familiarity (Baloglu & McCleary, 1999). As AI systems hold vast amounts of information, when tourists interact with AI, such information may be filtered by algorithms and passed on through AI to tourists for them making travel choices (Tussyadiah, 2020). This means that as one of the channels for grasping destination information, travel app users are likely to obtain a new perceived image of the destination when interacting with AI. In fact, several studies on the correlation between AI and destination image more or less support this viewpoint (Al-Bourini et al., 2021; Ceylan et al., 2021; Jalilvand & Heidari, 2017; Wang et al., 2020).

Moreover, the human-like design of AI is expected to bring affective acceptance to users (Samani, 2016). Affective image, as an important dimension of perceived image composition (Santana & Sevilha Gosling, 2018), would also constitute a certain logical connection between AI interactive approach and perceived image. For example, the deep interactive behavior guided by playfulness can create a moody pleasure for system users (Woszczynski et al., 2002). This will reduce users’ resistance to AI recommendations and establish an affective perception of relevant destinations when AI conducts destination marketing. In the process of destination image marketing, the AI customer service of the travel app plays the role of a sharer, whose personalized information guidance not only enhances users’ cognition of a destination (Kavoura et al., 2021), but also makes users to empathize with other tourists who have similar expectations of the destination (Pelau et al., 2021). To some extent, this indicates that AI interactions on travel apps can influence users’ cognition and affection regarding tourism destinations, thus forming composite perceived images.

However, due to the dynamic complexity of the mental construct of perceived image, it is frequently difficult to measure it in a single dimension (Hunter, 2016). San Martín and Rodríguez del Bosque (2008) argue that the formation of perceptual images is a dynamic process, and that the basic perception of destinations by tourists relies on the generation of cognitive and affective images. The use of cognitive image and affective image in understanding the specific composition of perceived images is widely recognized by numerous scholars (Agapito et al., 2013; Baloglu & McCleary, 1999; Santana & Sevilha Gosling, 2018). Baloglu and Mangaloglu (2001) propose that under the effect of cognitive and affective mental constructions, unique image formation ensues, thus dynamically reflecting the shifting state of the perceived image. As a result, perceived images have been described more as a cognitive-affective-unique structure with three dimension. Unique images are also frequently utilized as endpoints of perceived image composition to analyze correlation effects with other dimensions. Based on the complexity and persistence of processes in the composition of perceived images, this study hypothesizes:

H2. AI interactive approaches for travel apps have direct influences on users’ cognitive images of tourism destinations.

H2a. Interactive approach of informativeness is directly influencing users’ cognitive images.

H2b. Interactive approach of playfulness is directly influencing users’ cognitive images.

H2c. Interactive approach of personalization is directly influencing users’ cognitive images.

H3. AI interactive approaches for travel apps have direct influences on users’ affective images of tourism destinations.

H3a. Interactive approach of informativeness is directly influencing users’ affective images.

H3b. Interactive approach of playfulness is directly influencing users’ affective images.

H3c. Interactive approach of personalization is directly influencing users’ affective images.

H4. Travel apps users’ cognitive images have direct influences on their unique images.

H5. Travel apps users’ affective images have direct influences on their unique images.

On the other hand, tourists prefer destinations that best satisfy their psychological interests (Gartner, 1994). As a result, having a desirable and beautiful image of a destination is usually welcomed by a large number of tourists, and it also promotes their choices of revisiting or recommending (Agapito et al., 2013). In fact, there have been numerous viewpoints supporting the idea that tourists’ perceived image of a destination can more or less facilitate their travel choices (Al-Gasawneh & Al-Adamat, 2020; Beerli & Martín, 2004; J. S. Chen & Hsu, 2000; Liu et al., 2017; Santana & Sevilha Gosling, 2018). The reason for this is that tourists have formed a certain unique perceived images through the degree of cognition of the destination before making consumption decisions (Liu et al., 2020). Nowadays, this category of cognitive knowledge of destinations is entirely available from artificial intelligence of travel apps. On this basis, this study concludes with the following hypothesis:

H6. Travel apps users’ unique images have direct influences on their travel choices.

In addition to direct effects, there may be indirect effects during the composition of the perceived image and its influence on subsequent dimensions. Santana and Sevilha Gosling (2018) analyzed the mediating effects of cognitive image, affective image, and unique image on the antecedents and after-effects of perceived image formation with a valid sample of 396 formed Brazilian tourists. This result supports the mediating role of these dimensions. Several other studies have generated similar findings (Baloglu & McCleary, 1999; Huete-Alcocer et al., 2019; K. B. Kim & Aubrey, 2015), which provide a logical basis for the mediation hypothesis of this study. Thus, the mediation hypotheses for the dimensions of concern in this study are as follows:

H7. Users’ cognitive images mediate the relationship between AI interactive approaches and users’ unique images.

H8. Users’ affective images mediate the relationship between AI interactive approaches and users’ unique images.

H9. Users’ unique images mediate the relationship between users’ cognitive images and travel choices.

H10. Users’ unique images mediate the relationship between users’ affective images and travel choices.

In a nutshell, the role of the theoretical background is to explain various specific phenomena in a general way, so different theoretical explanations for the phenomenon to be studied can be identified by reviewing the literature, and then after selection and judgment, hypotheses can be derived that are consistent (Gladun, 1997). The hypotheses of this study rely on theory as logical support and possess sufficient comprehensiveness and accuracy. The establishment of hypotheses is the basis of hypothetical deductive quantitative research, and also provides the theoretical basis for the subsequent model construction and the establishment of variable dimensional relationships. Based on the theoretical background, the variables in this study’s model include three dimensions representing AI interactive approaches (informativeness, playfulness, and personalization), three dimensions reflecting the dynamic composition of the perceived image (cognitive image, affective image, and unique image), and travel choices. The logical relationship between the variables is shown in Figure 1.

Figure 1.

Research framework.

Research Methodology

Guided by the research framework, this study set up scales with each variable dimension for questionnaire data collection. On the basis of completing the reliability test and model fit test, linear regression analysis was conducted on the relevant data using structural equation modeling. The test results discuss the direct effects of AI interactive approaches on travel choices and the related effects on the process of perceived image composition. The indirect effects between the relevant variables are further explored using the three dimensions of perceived image components as mediators. The results of the analysis are compared with the previous hypotheses to test whether the hypotheses are established or not, and further conclusions are drawn on the basis of the data analysis.

The respondents in this research were asked to complete a questionnaire consisting of 30 main items, including 25 items on informativeness, playfulness, personalization, cognitive images, affective images, unique images, and travel choices. These items were measured using a 5-point Likert scale, ranging from 1- strongly disagree to 5- strongly agree. The remaining five items were statistics on the Socio-demographic characteristics of the respondents, including gender, age, education level, monthly income, and number of years of using the travel app. Table 1 illustrates the dimensions adapted from the relevant literature.

Table 1.

Dimensions Adapted From Literature.

Dimensions	Items and brief description	Source
Informativeness	IN1: AI provides diverse information.	Ho and See-To (2018), C. D. Huang et al. (2017)
	IN2: AI provides detailed information.
	IN3: AI provides useful information .
	IN4: Asking AI questions can get quick answers.
Playfulness	PL1: AI can tell me the trends in travel.	Byun et al. (2017), K. Chen and Yen (2004)
	PL2: Interaction with AI is fun.
	PL3: Interaction with AI is enjoyable.
	PL4: AI often promotes recreational activities.
Personalization	PE1: AI’s recommendations meet personal needs.	C. D. Huang et al. (2017), Chatterjee et al. (2019)
	PE2: AI help to share personal travel views.
	PE3: AI’s ads catch attention.
Cognitive images	CI1: Better understand natural environment.	Agapito et al. (2013), Santana and Sevilha Gosling (2018)
	CI2: Better understand facilities.
	CI3: Better understand humanity atmosphere.
	CI4: Better understand social cultures.
Affective images	AF1: Destination of AI presentation is pleasant	Agapito et al. (2013), Santana and Sevilha Gosling (2018)
	AF2: Destination of AI presentation is relaxing
	AF3: Destination of AI presentation is interesting.
	AF4: Destination of AI presentation is trendy.
Unique images	UI1: Introduced destinations with unique landscapes.	Santana and Sevilha Gosling (2018), Baloglu and Mangaloglu
	UI2: Introduced destinations with unique cultures.
	UI3: Introduced destinations with unique feelings.
Travel choices	TC1: Making travel decisions	Al-Gasawneh and Al-Adamat (2020), J. S. Chen and Hsu (2001)
	TC2:Selecting the right destination.
	TC3: Developing effective travel plans

Time requirements were installed for questionnaire identification. Respondents who took an unusual amount of time to complete the questionnaire probably failed to take the questionnaire seriously or did not read the questions properly before responding. To avoid an increase in error variance due to these non-careful respondents, 5.2 min was set as the cut-off time for inclusion in the analysis in this study.

In addition to the main items, the questionnaire was designed with three discriminatory items to identify whether the respondent was compliant or not. For example, if the respondent has a personal account or user ID on one of the major travel apps in China, the questionnaire would be closed if the respondent selects “No.” The inclusion of the identification items can significantly improve the precision of the samples, which enabled genuinely eligible respondents to take part in this investigation.

This study uses methods of data analysis such as reliability testing, model fitting analysis, structural equation modelling, and path analysis. Thereinto, it uses Amos 22.0 to test the stability and reliability of the measurement instruments, and the validity of the scales. Moreover, it uses structural equation modelling (SEM) to observe the fitness of the model to the data, and the significance of the model’s path coefficients is tested by partial least squares (PLS).

Research Findings

Sample Structure

The sample consists of users of the top six leading travel apps in China, including Qunar, Ctrip, Flying Pig, Tuniu, Tongcheng Travel, and Mafengwo. These six brands dominate the mobile travel market in China, accounting for almost 80% of the online booking market share (Lv et al., 2020; M. Li et al., 2021). This means that these users as respondents are at least universal enough to be representative of the Chinese travel app user population. Moreover, these six apps have deployed AI-powered customer service systems. According to incomplete statistics, the number of Chinese travel app users exceeded 100 million in 2021 (Yuan et al., 2022). This provides a predicted value for the total population for a simple random sample and requires obtaining a minimum of 384 samples to qualify the investigation for generalizability (Krejcie & Morgan, 1970; Sim et al., 2022).

The questionnaires were generated through China’s largest online academic research platform, Questionnaire Star (www.wjx.cn), and promoted to the user social platform operated by the six major travel apps. To obtain the sample size more efficiently, the sampling was set up with a bonus mechanism. Respondents who completed the survey will receive a random cash reward. In addition, by promoting the questionnaire to five other eligible users to complete it in a snowballing manner, the promoter would receive an extra reward. This type of questionnaire promotion combined with incentives greatly improves the efficiency of questionnaire collection. It made this study to harvest a large number of questionnaires within the limited period.

The respondents were required to participate in the survey over a quarterly period from July 2022 to September 2022. In the meantime, a total of 869 questionnaires were collected. Of these, 831 questionnaires were tested as valid samples. The response time for all 831 questionnaires included in the analysis was within the preset standard of 5.2 min, with an average response time of 4.7 min. The number of valid questionnaires far exceeds the minimum value of the number needed 384. In line with the basic needs of sampling, more valid samples can truer reflect the different opinions of respondents, thus making the data tend to be normally distributed, which greatly enhances the objectivity of investigation (Hair, 2011). Hence, in order to ensure generalizability and credibility, all 831 valid samples were retained for specific quantitative analysis in this study.

Of the 831 questionnaires, the proportion of females and males was 59.44% and 36.56% respectively, with a higher proportion of females than males. The age group is mainly between 19 and 30 years old, with a share of 71.19%. Income was mainly concentrated between less than RMB 5,000 and between RMB 5,001 and 10,000, accounting for 38.21% and 35.49% respectively. It is noteworthy that the majority of respondents, 40.43% and 32.68%, have been using the travel app for 1–3 years and 3–5 years respectively. This is a convincing indication that the sample structure is in line with the objectives of the research targeted. Table 2 shows the uses’ socio-demographic characteristics.

Table 2.

Structure of Respondents.

Indicators	Items	%
Gender	Male	36.56
Gender	Female	59.44
Age	Under 18	10.23
	19–25	46.42
	26–30	25.37
	31–40	10.35
	Over 41	7.63
Education	High school, junior college and below	27.03
	Specialized college	31.24
	Bachelor’s degree	39.53
	Master’s degree and above	2.20
Monthly income	Below RMB 5,000	38.21
	RMB 5,001–10,000	35.49
	RMB 10,001–20,000	15.32
	RMB 20,001 and above	14.98
Travel app usage years	Less than 1 year	15.17
	1–3 years	40.43
	3–5 years	32.68
	More than 5 years	11.72

Results

Each measurement items relied on software analysis of Cronbach’s α to verify their reliability (Cronbach & Shavelson, 2004). The test results showed that each variable matched the reference values. This indicated the research model to be judged as reliable, as shown in Table 3.

Table 3.

Cronbach’s α Value Testing.

Variables	Items	Cronbach’s α
Informativeness	4	.83
Playfulness	4	.78
Personalization	3	.89
Cognitive images	4	.91
Affective images	4	.87
Unique images	3	.89
Travel choices	3	.86

Note. If the value of Cronbach’s α is between .6 and .7, then the measurement scale is acceptable, .7 to .8 = good reliability, and .8 to .9 = great reliability (Cronbach & Shavelson, 2004; Heo et al., 2015).

In this study, the degree of fitness between the observed model and the data is examined using structural equation modeling (SEM). Through the SEM in the Amos22.0 tool, the fit indexes, which including: x² to degree of freedom (x²/df), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), normed fit index (NFI), comparative fit index (CFI), root-mean-square error of approximation (RMSEA), were analyzed as follows: x²/df = 2.97, GFI = 0.92, AGFI = 0.91, NFI = 0.96, CFI = 0.89, RMSEA = 0.07. Except for the value of CFI, which is slightly lower than the standard of 0.90, all other fitness indicators satisfied the requirements of the basic standard. Thus, the observed data have a high fit to the model, the model structure is relatively robust, and the subsequent quantitative analysis is expected to have sufficient validity. The specific measurement criteria are shown in Table 4.

Table 4.

Model Fitting Standards and Results.

Fitness	Standards	Results
x²/df	<2 and <5	2.97
GFI	>0.9	0.92
AGFI	>0.8	0.91
NFI	>0.9	0.96
CFI	>0.9	0.89
RMSEA	<0.08	0.07

Note. The criteria for the fitness values refer to previous literature for guidance (Barrett, 2007; Weston & Gore, 2006).

The path coefficients of the effects of the variables were examined using PLS analysis. From the results of the analytical tests, all the path coefficients of direct effects are significant. The value of the explained variance (R²) for travel choice reached 0.68, indicating that the effects of AI interactive approaches and unique images on travel choices have favorable explanatory properties. The path coefficient of AI interactive approaches to cognitive images is equally significant by R² = 0.65. The variance explained by AI interactive approaches for affective images also reached R² = 0.61. The explainable variance for unique images, on the other hand, reached a high level of R² = 0.70. Figure 2 shows the coefficients and significance (at p < .01; p < .001) of each path.

Figure 2.

Impact path analysis.

Specifically, informativeness, playfulness, personalization, and unique images are significant factors influencing travel choices, explaining 68% of the variance variance in travel choices. The path coefficients of informativeness, playfulness, personalization, and unique images on tourism choice achieve statistical significance. All coefficients are significant at p < .001 except for the significant coefficient of playfulness on travel choices of 0.25 at the standard p < .01. Therefore, the hypotheses H1a, H1b, H1c, and H6 proposed in this study are verified.

Informativeness, playfulness, and personalization are important antecedent variables affecting cognitive images, explaining 65% of the variance in cognitive images. The path coefficients of informativeness, playfulness, and personalization to cognitive images are 0.28 (p < .01), 0.32 (p < .001), and 0.37 (p < .001). Respectively, all of which reached statistical significance. The hypotheses H2a, H2b, and H2c are verified.

In the results of the impact analysis for the other dimension of perceived image, informativeness, playfulness, and personalization still produced significant positive effects. Their combined explanatory capacity for affective images reached R² = 0.61 with path coefficients of H3a = 0.36 (p < .001), H3b = 0.29 (p < .01), and H3c = 0.25 (p < .01), respectively. Therefore, all three hypotheses are equally supported.

The hypotheses H4 and H5 were used to test the interaction of the internal components of the perceived images. In this category, the coefficient influence of cognitive images on unique images is 0.43, which is significant at the criterion of p < .001. The coefficient influence of affective images on unique images is 0.33, significant at the same standard. Both had strong positive effects on unique images (R² = 0.70), hypotheses H4 and H5 are confirmed.

In addition, this study examines the mediating effects via the three constituent dimensions of perceived images (cognitive images, affective images, and unique images). These three dimensions are used as a mediating variable to further examine the indirect effect of the relationship between AI interactive approaches on travel choices. Among them, only two mediating effects were not significant, which were cognitive images mediated effects on personalization and unique images, and affective images mediated effects on personalization and unique images. so hypotheses H7 and H8 were partially confirmed. When variable unique images was tested as a mediator, both effects in correlation produced significant results, so hypotheses H9 and H10 were confirmed. The detailed results of the mediated test analysis are shown in Table 5.

Table 5.

Mediation Analysis Results.

Hypotheses	Mediators	β	T	p	R ²	Results
H7	IN → CI → UI	.39	3.66	.001	0.63	Partially supported
	PL → CI → UI	.37	3.89	.001	0.59
	PE → CI → UI	.02	1.12	.153	0.18
H8	IN → AF → UI	.38	3.27	.001	0.61	Partially supported
	PL → AF → UI	.29	2.36	.010	0.55
	PE → AF → UI	.13	1.42	.174	0.22
H9	CI → UI → TC	.45	3.63	.001	0.66	Supported
H10	AF → UI → TC	.27	2.86	.010	0.54	Supported

Note. IN = Informativeness; PL = Playfulness; PE = Personalization; CI = Cognitive Images; AF = Affective Images; UI = Unique Images; TC = Travel Choices.

β indicates Standardized Coefficient; T indicates regression t-coefficient.

p indicates significance, the minimum criterion for significance is p < .05.

R ² indicates explanatory capacity.

Discussion

The findings of this study validate the proposed research framework, and the analysis supports the hypothesis of the relationship between the variables. First, informative (β = .34, p < .001), playful (β = .25, p < .01), and personalized (β = .34, p < .001), these AI interactive approaches directly impact on travel app users to generate travel choices. The users interact with AI to receive satisfactory destination information, add fun, and personalize their own needs. In fact, these forms of interaction provide enhanced assistance to users at all stages of forming travel choices. One possible explanation for these findings resides in the growing acceptance by humans of the role of AI technology as an assistant in the service sector, with people increasingly enjoying the convenience of AI in an ever-richer mode of interaction (Hernandez-Ortega & Ferreira, 2021).

Furthermore, six relationships between AI interactive approaches and perceived images are supported in the model; informativeness, playfulness, and personalization are strictly correlated with cognitive images and affective images. On the other hand, cognitive images and affective images also partially mediated the relationship between AI interactive approaches and unique images. Although the indirect effects of personalization and unique image were not confirmed in the validation, the insignificance of the mediating component can be ignored since the direct effects produced good results.

These results are consistent with some literature sharing the view that tourists’ perceived images of a destination can be influenced in knowledge shared by AI (Deng & Li, 2018; Miguel & Huertas, 2022; Tavitiyaman et al., 2021). Hence, travel app users are motivated to use the AI services provided by the system to access destinations information as well as to express their perceived images. From the point of view of tourism development in China, the mainstream apps have AI technologies that provide interesting services, and the marketing of travel service providers is more or less dominated by AI algorithms about marketing. This means that AI is becoming the new hub between users, travel service providers, and tourism destinations. Thus, users receive the convenience of AI services based on this new way of obtaining information and generating impressions of a destination, and then choosing a possible destination for traveling.

In fact, the travel app users’ unique images of destinations have a clear impact on their travel choices is further confirmed (β = .38, p < .001). Hence, improving tourists’ perceived images of destinations by developing destination-related marketing campaigns through AI can play a critical role in tourists’ travel intentions. Travel apps provide a favorable platform, while AI technology simplifies and personalizes the information delivered by marketing campaigns to the needs of users. These are all advantages that can be exploited by tour operators.

Furthermore, the perceived images mediate the impact of informative, playful, and personalized AI interactive approaches on user-generated travel choices. Users use travel apps to make travel decisions with the primary goal of searching for useful information for their travel plans. The well-balanced relationship that AI builds with users through interaction can help them accept the image of the recommended destination. As stakeholders, AI customer service providers are able to refine their data acquisition management processes and use them to guide CRM implementation and operations (Syvänen & Valentini, 2020). In the continuous development of technology interaction management model, the combination of human and machine management will also gradually get comprehensive mechanism construction.

The findings suggest that newly developed local tourism apps should place more emphasis on building AI service models through entertainment. The AI-led virtual tours or other entertaining promotions help users to have a deeper understanding and feeling of local tourism. In this way, local tour operators contribute to the overall promotion of the destination by deepening users’ sense.

Conclusions

Technological advances in AI have provided travel apps with new customer service tools, which brings together in-depth information regarding tourism destinations and delivers it to users in an anthropomorphic way. On the other hand, it has become more common for travelers to make travel plans through travel apps, and the massive increase in customer demand has slowed down the response time of manual customer service in marketing and response. This study attempts to model the use of travel choices delivered as perceived images in AI interactions in order to understand the effectiveness of AI in working with travel apps.

This study enriches the theoretical structure by extending the interrelationship between technology and decision making using the perceived images as a fulcrum, possessing sufficient academic significance. In particular, the results of the investigation provide valuable insights. Users’ acceptance and use of AI’s intelligent support can help them deepen their cognitive and affective connections to desired destinations, and also motivate them to choose these destinations for travel activities. This supports other perspectives that address AI interaction research (Tavitiyaman et al., 2021).

In addition, this study explores the process by which the travel choices of travel app users using AI customer service are influenced in a modeling approach. This has important theoretical implications for understanding the support of AI technology for tourism marketing. The results of the study pay particular attention to the important role played by users in forming perceived images of destinations. Therefore, these results can be considered as one of the attempts to apply tourism destination image theory in new technologies.

On the other hand, this study also provides a realistic perspective to contribute to the arguments needed for the development of the various groups related to tourism apps, which has a certain relevance. The implications of the study findings are not limited to targeting the needs of travel app users. In fact, for tourism service providers working on tourism marketing through travel apps, this study also helps them to gain insight into the behavioral intentions of tourists under the influence of new technologies. In the near future, AI technology will inevitably permeate different parts of the tourism industry, providing practitioners with assistance in product development, marketing, and service (Doborjeh et al., 2022).

In this trend, marketers in the tourism industry will be more involved in marketing activities that focus on AI interaction, relieving the marketing pressure of human service staff. In fact, some routine questions are answered to users via AI, which can instead provide clear and objective results. Therefore, standardized management of customer relationship marketing, of which AI is an important part, is also something that the tourism industry needs to focus on and discuss.

There are still some limitations in this study. The scope of the study is limited to mainstream travel app users in China only. Although this has some general value from a technical point of view, for the sake of academic rigor, future studies in other regions are needed to further compare or corroborate, and to enrich the existing theoretical framework more comprehensively.

For subsequent practicing researchers, this study can provide some appropriate theoretical and data support. In the future, it can also further focus on the research of AI interactive approaches in conjunction with other factors. This is due to the fact that both perceived image and travel choice are complex psychological construction processes (Santana & Sevilha Gosling, 2018; Schneider & Leyer, 2019). This kind of empirical research for socialization can further adjust the interactive strategies for the designers of tourism AI. Moreover, this study used more mature quantitative software in the selection of data analysis tools, and with the development of statistics, follow-up researchers may consider utilizing more novel tools like Python for their analyses.

In conclusion, the innovative perspective of this study is to explore the impact of AI interactive technology on the promotion of tourism destination image and tourist decision making, which has not been adequately discussed in conjunction with existing research. The framework formulated by the study is a logical support for the relationship between AI interaction strategies, tourism decision-making behavior, and perceptual images, effectively extending the logical connections of established theories. The framework, although concise, can effectively guide other related studies of a similar nature. It not only integrates multidisciplinary thinking to form a three-dimensional theoretical structure with conceptual theoretical value, but also supports the adoption of AI technology in the tourism industry by analyzing practical applications to connect tourism destinations with tourists in an innovative manner. From the psychological path, the study contributes to the understanding of the linear constitutive relationship between AI interactive technologies, perceived images, and travel choices. Therefore, this study is worthy of being studied in related disciplines.

Footnotes

Acknowledgements

This research acknowledges the assistance and support of School of Tourism, Hospitality & Environmental Management, Universiti Utara Malaysia, and Hezhou University in Guangxi, China in carrying out the authors’ academic work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethics Statement

This research does not address issues such as the ethics of animals or humans.

ORCID iD

Tianyang Luo

Data Availability Statement

The data supporting the results of this study are available from the corresponding author on reasonable request.

References

Agapito

Oom

Valle

da Costa Mendes

(2013). The cognitive-affective-conative model of destination image: A confirmatory analysis. Journal of Travel & Tourism Marketing, 30(5), 471–481.

Al-Bourini

F. A.

Aljawarneh

N. M.

Almaaitah

M. F.

Altahat

Alomari

Z. S.

Sokiyna

(2021). The role of e-word of mouth in the relationship between online destination image, e-satisfaction, e-trust & e-service quality for international tourists perception. Journal of Information Technology Management, 13, 92–110.

Al-Gasawneh

Al-Adamat

(2020). The relationship between perceived destination image, social media interaction and travel intentions relating to NEOM city. Academy of Strategic Management Journal, 19(2), 1–12.

Allen

J. F.

Byron

D. K.

Dzikovska

Ferguson

Galescu

Stent

(2001). Toward conversational human-computer interaction. AI Magazine, 22(4), 27–38.

Arentze

T. A.

(2013). Adaptive personalized travel information systems: A Bayesian method to learn users’ personal preferences in multimodal transport networks. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1957–1966.

Askool

Nakata

(2011). A conceptual model for acceptance of social CRM systems based on a scoping study. AI & Society, 26(3), 205–220.

Balakrishnan

Dwivedi

Y.K.

Hughes

Boy

(2021). Enablers and inhibitors of AI-powered voice assistants: A dual-factor approach by integrating the Status Quo Bias and technology acceptance model. Information Systems Frontiers. Retrieved from https://link.Springer.com/article/10.1007/s10796-021-10203-y.

Baloglu

Mangaloglu

(2001). Tourism destination images of Turkey, Egypt, Greece, and Italy as perceived by US-based tour operators and travel agents. Tourism Management, 22(1), 1–9.

Baloglu

McCleary

K. W.

(1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897.

10.

Barrett

(2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815–824.

11.

Beerli

Martín

J. D.

(2004). Tourists’ characteristics and the perceived image of tourist destinations: A quantitative analysis-a case study of Lanzarote, Spain. Tourism Management, 25(5), 623–636.

12.

Beritelli

Reinhold

Luo

(2019). “How come you are here?” Considering the context in research on travel decisions. Journal of Travel Research, 58(2), 333–337.

13.

Byun

K. A.

Dass

Kumar

Kim

(2017). An examination of innovative consumers’ playfulness on their pre-ordering behavior. Journal of Consumer Marketing, 34(3), 226–240.

14.

Cain

L. N.

Thomas

J. H.

Alonso

Jr . (2019). From sci-fi to sci-fact: the state of robotics and AI in the hospitality industry. Journal of Hospitality and Tourism Technology, 10(4), 624–650.

15.

Ceylan

Çizel

Karakaş

(2021). Destination image perception patterns of tourist typologies. International Journal of Tourism Research, 23(3), 401–416.

16.

Chatterjee

Ghosh

S. K.

Chaudhuri

Nguyen

(2019). Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, 32(2), 144–157.

17.

Chen

J. S.

Hsu

C. H. C.

(2000). Measurement of Korean tourists’ perceived images of overseas destinations. Journal of Travel Research, 38(4), 411–416.

18.

Chen

Yen

D. C.

(2004). Improving the quality of online presence through interactivity. Information & Management, 42(1), 217–226.

19.

Cheriyan

Sharma

R. K.

Joseph

Kappil

S. R.

(2022). Consumer acceptance towards AI-enabled chatbots; case of travel and tourism industries. Journal of Positive School Psychology, 6(3), 3880–3889.

20.

Choi

Wang

Sparks

(2019). Travel app users’ continued use intentions: It’s a matter of value and trust. Journal of Travel & Tourism Marketing, 36(1), 131–143.

21.

Cronbach

L. J.

Shavelson

R. J.

(2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64(3), 391–418.

22.

De Gruyter

Rose

Currie

Rye

van de Graaff

(2017). Travel plans for new developments: a global review. Transport Reviews, 38(2), 142–161.

23.

Deng

X. (Robert)

. (2018). Feeling a destination through the “right” photos: A machine learning model for DMOs’ photo selection. Tourism Management, 65, 267–278.

24.

Dey

Shukla

(2020). Analytical study on use of AI techniques in tourism sector for smarter customer experience management. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). Retrieved from https://ieeexplore.ieee.org/abstract/document/9132925.

25.

Gkikas

D. C.

Theodoridis

P. K.

(2019). Artificial intelligence (AI) impact on digital marketing research. Strategic Innovative Marketing and Tourism, 1251–1259.

26.

Doborjeh

Hemmington

Doborjeh

Kasabov

(2022). Artificial intelligence: A systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1176.

27.

Ferreira

Lefèvre

(2015). Reinforcement-learning based dialogue system for human-robot interactions with socially-inspired rewards. Computer Speech & Language, 34(1), 256–274.

28.

García-Serrano

A. M.

Martínez

Hernández

J. Z.

(2004). Using AI techniques to support advanced interaction capabilities in a virtual assistant for e-commerce. Expert Systems with Applications, 26(3), 413–426.

29.

Gartner

W. C.

(1994). Image formation process. Journal of Travel & Tourism Marketing, 2(2–3), 191–215.

30.

Gladun

V. P.

(1997). Hypothetical modeling: Methodology and application. Cybernetics and Systems Analysis, 33(1), 7–15.

31.

González

López

Rosa

J. L.

(2004). Smart user models for tourism: A holistic approach for personalized tourism services. Information Technology & Tourism, 6(4), 273–286.

32.

Grundner

Neuhofer

(2021). The bright and dark sides of artificial intelligence: A futures perspective on tourist destination experiences. Journal of Destination Marketing & Management. https://www.sciencedirect.com/science/article/pii/S2212571X20301335.

33.

James

E. L.

(1998). Interactivity reexamined: A baseline analysis of early business web sites. Journal of Broadcasting & Electronic Media, 42(4), 457–474.

34.

Hair

J. F.

(2011). Multivariate data analysis: An overview. International Encyclopedia of Statistical Science, 904–907.

35.

Heggie

I. G.

(1978). Putting behaviour into behavioural models of travel choice. Journal of the Operational Research Society, 29(6), 541–550.

36.

Heo

Kim

Faith

M. S.

(2015). Statistical power as a function of Cronbach alpha of instrument questionnaire items. BMC Medical Research Methodology, 15(1). https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-015-0070-6#citeas

37.

Hernandez-Ortega

Ferreira

(2021). How smart experiences build service loyalty: The importance of consumer love for smart voice assistants. Psychology & Marketing, 38(7), 1122–1139.

38.

Hoang

D. S.

Phan

M. D.

(2021). Service innovation in customer intelligence from ai perspective: A smart framework for tourist customer experiences. HUFLIT Journal of Science, 6(3), 53–57.

39.

K. K. W.

See-To

E. W. K.

(2018). The impact of the uses and gratifications of tourist attraction fan page. Internet Research, 28(3), 587–603.

40.

Huang

Velasco

E. D. M.

Haney

Alvarez

(2022). The future of destination marketing organizations in the insight era. Tourism and Hospitality, 3, 803–808.

41.

Huang

C. D.

Goo

Nam

Yoo

C. W.

(2017). Smart tourism technologies in travel planning: The role of exploration and exploitation. Information & Management, 54(6), 757–770.

42.

Huang

M. H.

Rust

R. T.

(2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41.

43.

Huete-Alcocer

Martinez-Ruiz

M. P.

López-Ruiz

V. R.

Izquiedo-Yusta

(2019). Archeological tourist destination image formation: Influence of information sources on the cognitive, affective and unique image. Frontiers in Psychology, 10, 1–13.

44.

Hunter

W. C.

(2016). The social construction of tourism online destination image: A comparative semiotic analysis of the visual representation of Seoul. Tourism Management, 54, 221–229.

45.

Ivanov

Webster

(2019). Conceptual framework of the use of robots, artificial intelligence and service automation in travel, tourism, and hospitality companies. Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 1, 7–37.

46.

Jabeen

Al Zaidi

Al Dhaheri

M. H.

(2022). Automation and artificial intelligence in hospitality and tourism, Tourism Review, 77(4), 1043–1061.

47.

Jalilvand

M. R.

Heidari

(2017). Comparing face-to-face and electronic word-of-mouth in destination image formation. Information Technology & People, 30(4), 710–735.

48.

Jarrar

Awobamise

A. O.

Sellos

P. S.

(2020). Technological readiness index (TRI) and the intention to use smartphone apps for tourism: A focus on in Dubai mobile tourism app. International Journal of Data and Network Science, 4, 297–304.

49.

Jiang

Karran

A. J.

Coursaris

C. K.

Léger

Beringer

(2022). A situation awareness perspective on human-AI interaction: Tensions and opportunities, International Journal of Human-Computer Interaction. https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2093863

50.

Jiao

E. X.

Chen

J. L.

(2019). Tourism forecasting: A review of methodological developments over the last decade. Tourism Economics, 25(3), 469–492.

51.

Kavoura

Havlovic

S. J.

Totskaya

(Eds.). (2021). Strategic innovative marketing and tourism in the COVID-19 era. Springer Proceedings in Business and Economics. https://link.springer.com/book/10.1007/978-3-030-66154-0

52.

Kim

Giroux

Lee

J. C.

(2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 1140–1155.

53.

Kim

Kang

Bae

(2022). Human likeness and attachment effect on the perceived interactivity of AI speakers. Journal of Business Research, 144, 797–804.

54.

Kim

K. B.

Aubrey

J. S.

(2015). A cross-cultural comparison of cognitive and affective mediators in the relationship between media use and body image disturbance: Focusing on US and Korean women. Asian Journal of Communication, 25(5), 507–524.

55.

Koo

Xiang

Gretzel

Sigala

(2021). Artificial intelligence (AI) and robotics in travel, hospitality and leisure. Electronic Markets, 31, 473–476.

56.

Krejcie

R. V.

Morgan

D. W.

(1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.

57.

Lalicic

Weismayer

(2021). Consumers’ reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents. Journal of Business Research, 129, 891–901.

58.

Leung

X. Y.

(2019). Technology-enabled service evolution in tourism: a perspective article. Tourism Review, 75(1), 279–282.

59.

Lee

K. Y.

Emokpae

Yang

S.-B.

(2021). What makes you continuously use chatbot services? Evidence from Chinese online travel agencies. Electronic Markets, 31, 575–599.

60.

Yin

Qiu

Bai

(2021). A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management. https://www.sciencedirect.com/science/article/abs/pii/S0278431921000736.

61.

Liao

Bashir

A. K.

Mumtaz

Jolfaei

Kvedaraite

(2020). Cognitive popularity based AI service sharing for software-defined information-centric networks. IEEE Transactions on Network Science and Engineering, 7(4), 2126–2136.

62.

Liu

J. (Justin)

Kim

W. G.

(2017). The role of travel experience in the structural relationships among tourists’ perceived image, satisfaction, and behavioral intentions. Tourism and Hospitality Research, 17(2), 135–146.

63.

Liu

Mehraliyev

Liu

Schuckert

(2020). The roles of social media in tourists’ choices of travel components. Tourist Studies, 20(1), 27–48.

64.

Yang

(2020). Understanding the emergence and development of online travel agencies: A dynamic evaluation and simulation approach. Internet Research, 30(6), 1783–1810.

65.

Miguel

Huertas

(2022). Technological attributes that predict tourists’ intention to visit destination, recommend and destination image: Empirical evidence from the Malaga Chatbot. Advances in Tourism, Technology and Systems. https://link.springer.com/chapter/10.1007/978-981-19-1040-1_13

66.

Miller

(2019). The intrinsically linked future for human and Artificial Intelligence interaction. Journal of Big Data. https://link.springer.com/article/10.1186/s40537-019-0202-7

67.

Mirra

Pugnale

(2022). Expertise, playfulness and analogical reasoning: three strategies to train Artificial Intelligence for design applications. Architecture, Structures and Construction, 2, 111–127.

68.

Parvez

M.O.

Arasli

Ozturen

Lodhi

R.N.

Ongsakul

(2022). Antecedents of human-robot collaboration: Theoretical extension of the technology acceptance model. Journal of Hospitality and Tourism Technology, 13(2), 240–263.

69.

Pelau

Dabija

D.-C.

Ene

(2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior. https://www.sciencedirect.com/science/article/abs/pii/S0747563221001783

70.

Pike

Page

S. J.

(2014). Destination marketing organizations and destination marketing: A narrative analysis of the literature. Tourism Management, 41, 202–227.

71.

Phaosathianphan

Leelasantitham

(2019). Understanding the adoption factors influence to the use of intelligent travel assistant (ITA) for eco-tourists: An extension of the UTAUT. International Journal of Innovation and Technology Management. https://www.worldscientific.com/doi/abs/10.1142/S0219877019500603

72.

Rzepka

Berger

(2018). User interaction with AI-enabled systems: A systematic review of IS research. International Conference on Information Systems. https://www.researchgate.net/publication/329269262_User_Interaction_with_AI-enabled_Systems_A_Systematic_Review_of_IS_Research

73.

Samala

Katkam

B. S.

Bellamkonda

R. S.

Rodriguez

R. V.

(2022). Impact of AI and robotics in the tourism sector: a critical insight. Journal of Tourism Futures, 8(1), 73–87.

74.

Samani

(2016). The evaluation of affection in human-robot interaction. Kybernetes, 45(8), 1257–1272.

75.

San Martín

Rodríguez del Bosque

I. A.

(2008). Exploring the cognitive–affective nature of destination image and the role of psychological factors in its formation. T ourism Management, 29(2), 263–277.

76.

Santana

L. D.

Sevilha Gosling

M. D.

(2018). Dimensions of image: A model of destination image formation. Tourism Analysis, 23(3), 303–322.

77.

Schneider

Leyer

(2019). Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions. Managerial and Decision Economics. https://onlinelibrary.wiley.com/doi/abs/10.1002/mde.2982

78.

Sim

Kim

S.-Y.

Suh

(2022). Sample size requirements for simple and complex mediation models. Educational and Psychological Measurement, 82(1), 76–106.

79.

Sudapet

I. N.

Nasihien

R. D.

Setiawan

M. I.

Zulkifli

C. Z.

(2021). E-wisata android app designed for tourism, regional GDP, and tax of regional government budget in Indonesia: The context of indistrial revolution 4.0. International Journal of eBusiness and eGovernment Studies, 13(1), 202–219.

80.

Sundar

S. S.

(2020). Rise of Machine Agency: A framework for studying the psychology of Human–AI Interaction (HAII). Journal of Computer-Mediated Communication. https://academic.oup.com/jcmc/article/25/1/74/5700811

81.

Syvänen

Valentini

(2020). Conversational agents in online organization-stakeholder interactions: A state-of-the-art analysis and implications for further research. Journal of Communication Management, 24(4), 339–362.

82.

Tavitiyaman

Tsang

W. L.

Lam

C. R.

(2021). The influence of smart tourism applications on perceived destination image and behavioral intention: The moderating role of information search behavior. Journal of Hospitality and Tourism Management, 46, 476–487.

83.

Tussyadiah

(2020). A review of research into automation in tourism: Launching the annals of tourism research curated collection on artificial intelligence and robotics in tourism. Annals of Tourism Research. https://www.sciencedirect.com/science/article/pii/S016073832030027X

84.

VanNuenen

Scarles

(2021). Advancements in technology and digital media in tourism. Tourist Studies, 21(1), 119–132.

85.

Wang

Luo

Huang

S. (Sam)

. (2020). Developing an artificial intelligence framework for online destination image photos identification. Journal of Destination Marketing & Management. https://www.sciencedirect.com/science/article/abs/pii/S2212571X20301347

86.

Wenskovitch

North

(2020). Interactive artificial intelligence: Designing for the “two black boxes” problem. Computer, 53(8), 29–39.

87.

Westerman

Edwards

A. P.

Edwards

Luo

Spence

P. R.

(2020). I-it, i-thou, i-robot: The perceived humanness of AI in human-machine communication. Communication Studies. https://www.tandfonline.com/doi/abs/10.1080/10510974.2020.1749683

88.

Weston

Gore

P. A.

(2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751.

89.

Wiener

D. N.

(1948). Subtle and obvious keys for the Minnesota Multiphasic Personality Inventory. Journal of Consulting Psychology, 12(3), 164–170.

90.

Wirtz

Kunz

W. H.

Paluch

(2021). The service revolution, intelligent automation and service robots. The European Business Review. https://www.researchgate.net/publication/346392669_The_Service_Revolution_Intelligent_Automation_and_Service_Robots

91.

Wong

I. A.

Law

Zhao

X. (Roy)

. (2016). When and where to travel a longitudinal multilevel investigation on destination choice and demand. Journal of Travel Research, 56(7), 868–880.

92.

Woszczynski

A. B.

Roth

P. L.

Segars

A. H.

(2002). Exploring the theoretical foundations of playfulness in computer interactions. Computers in Human Behavior, 18(4), 369–388.

93.

Huang

S. (Sam)

(2019). Time, money, or convenience: What determines Chinese consumers’ continuance usage intention and behavior of using tourism mobile apps? International Journal of Culture, Tourism and Hospitality Research, 13(3), 288–302.

94.

Yuan

Chan

C.-S.

Eichelberger

Pikkemaat

(2022). The effect of social media on travel planning process by Chinese tourists: The way forward to tourism futures, Journal of Tourism Futures. https://www.emerald.com/insight/content/doi/10.1108/JTF-04-2021-0094/full/html

95.

Zhang

Sun

(2021). The application of artificial intelligence technology in the tourism industry of Jinan. Journal of Physics: Conference Series. https://iopscience.iop.org/article/10.1088/1742-6596/1302/3/032005/meta

96.

Zhang

Kim

J. (Jamie)

Kim

Fesenmaier

D. R.

(2019). The tourism story project: Developing the behavioral foundations for an AI supporting destination story design. E-Review of Tourism Research, 17(2), 169–187.

97.

Zhang

Muskat

Law

(2021). Tourism demand forecasting a decomposed deep learning approach. Journal of Travel Research, 60(5), 981–997.

Travel Choices and Perceived Images Influenced by AI Interactive Approaches of Travel Apps: An Evidence From Chinese Mobile Travel Users

Abstract

Keywords

Introduction

Theoretical Background

Dimensions of AI Interactive Approaches

Theoretical Linkages to Travel Choices

Theoretical Linkages to Perceived Images

Research Methodology

Research Findings

Sample Structure

Results

Discussion

Conclusions

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Ethics Statement

ORCID iD

Data Availability Statement

References