A study on the influencing factors of tourists’ travel intentions: a configurational analysis based on fsQCA

Tourism, as a pillar industry of the national economy, has made significant contributions to economic growth, employment, and the improvement of residents’ living standards (Lu, 2024). In 2023, there was a substantial increase in both the number of trips and expenses in China, underscoring the immense potential and demand within the tourism market. With the advancement of network technology, tourists' decision-making processes are undergoing transformation, making online attention a crucial factor in tourism marketing and tourists’ experiences (Dong et al., 2023). The widespread adoption of network technology has enabled tourists to access information more autonomously and transparently, thereby facilitating travel decision-making (Chon and Hao, 2024).

In the context of tourism consumption decision-making, destination image is a critical factor directly influencing tourists’ expectations, perceptions, and travel intentions (Birenboim et al., 2023). A positive destination image not only enhances the competitiveness and sustainable development of the destination (Maghrifani et al., 2022), but also strengthens tourists’ sense of trust and reduces uncertainty in the travel process, thereby increasing satisfaction (Lam et al., 2020). When visitors have a positive view of a destination, they want to have a good emotional experience during their visit and are eager to create unforgettable memories. This further enhances their motivation to choose that destination (Ragb et al., 2020). Additionally, perceived value is a crucial factor influencing tourists’ travel behavioral intentions (Zhu et al., 2024). Perceived value refers to tourists’ subjective evaluation of the overall benefits provided by a tourism product or service, covering dimensions such as functional value and social value (Arslanagic-Kalajdzic and Zabkar, 2017). Functional value is reflected in the convenience and efficiency services provided by the destination, meeting basic needs (Wang et al., 2024b), while social value affects the willingness to travel by satisfying tourists’ social and identity needs (Su and Chen, 2022).

However, previous research has primarily focused on the impact of single factors on travel intentions, often overlooking the combined effects of multiple factors. In the real-world and complex tourism decision-making process, specific configurations of factors such as online attention, destination image, and perceived value may stimulate or inhibit tourists’ travel intentions. It is necessary to construct a comprehensive theoretical framework to analyze these interactions and reveal the configurational effects of multiple factors, thereby providing valuable theoretical guidance for tourism marketing practices. Therefore, this study employs the fsQCA (fuzzy-set qualitative comparative analysis) method, which is advantageous for revealing how combinations of factors jointly lead to the same outcome (Geremew et al., 2024).

This study explores the joint impact of online attention, destination image, and perceived value on travel intentions across various configurations. It poses critical questions: How do these factors interact to shape tourists’ intentions? How does this multi-factor approach overcome the constraints of previous single-factor analyses? Which combinations of these factors are pivotal in driving high or low travel intentions? The study aims to elucidate how these elements interact and how a multi-factor model enhances our understanding of travel intention formation, especially compared to the effects that were not previously discussed.

Tourists’ travel intentions refer to the inclination or desire of individuals or groups to engage in travel or participate in tourism activities. This concept can be seen as a special kind of consumer tendency. However, unlike general commodities, tourism activities are highly experiential and contextual in nature (Tassiello and Tillotson, 2020). Most scholars understand travel intentions as the likelihood of tourists visiting or recommending a destination, and it serves as a bridge and intermediary between tourism motivation and actual tourism behavior (Bianchi, 2022; Ragb et al., 2020).

Existing research on tourists’ travel intentions primarily focuses on three aspects. The first aspect is the impact of the new media environment. The internet and social media provide new channels for the dissemination of tourism information, influencing tourists’ decision-making. New forms of media such as live streaming, short videos, and online reviews enhance the attractiveness of destinations and promote travel intentions (Ghaderi et al., 2019). The second aspect is the role of tourists’ psychological perceptions. Tourists’ cognition and emotional experiences of destination image and service quality significantly affect their intentions to revisit and recommend (Zhou et al., 2024). Perceived usefulness and pleasure, as cognitive and affective responses, determine travel decisions. The third aspect is the travel intention under special circumstances. Special tourism activities or unexpected events can affect travel intentions (Yu et al., 2024). For instance, the sense of self-efficacy in low-carbon tourism and the portrayal of destinations in films and TV shows both have an impact on travel intentions (Shu and Tan, 2022).

Online attention refers to the degree of focus and interest that individuals or groups have towards specific topics, events, or content on the internet (Song et al., 2022). In the digital age, this type of attention is increasingly reflected in online behaviors such as searching, browsing, sharing, and commenting. In the field of tourism, online attention specifically refers to the extent to which tourists pay attention to destinations, products, and services through social media and online travel sites (Kim et al., 2019). Online attention can be divided into two main aspects: online information attention and online social attention. Online information attention focuses on individuals’ attention and interest in various types of online information, with research mainly exploring how information characteristics (such as usefulness, novelty, emotional tone, etc.) and presentation modes (such as videos, illustrations, live broadcasts, etc.) affect the level of attention (Ghaderi et al., 2019). Online social attention, on the other hand, focuses on individuals’ interactions and attentional behaviors within social networks (Lam et al., 2020), and research has explored the impact of individual traits (such as proactivity, openness) and social network structures (such as network size, strength of ties) on social attention (Zhang et al., 2018).

However, existing studies primarily focus on the characteristics of information and individual behaviors on social media, lacking a comprehensive analysis of the relationship between online informational attention and online social attention. Particularly, how online attention shapes positive or negative destination images and influences tourists' travel intentions through the configurational effects of multiple factors is a topic that warrants in-depth research.

Destination image refers to the overall impression and perception that tourists or potential tourists have of a particular travel destination. It is a complex, comprehensive concept that includes both cognitive and emotional aspects. Existing research has conducted in-depth studies on it from three aspects: tourists' decision-making, satisfaction, and intention to revisit. Firstly, a positive destination image is conducive to attracting more visitors. A favorable and attractive destination image often entices tourists to travel there (Zhang et al., 2018). Secondly, a good destination image can enhance tourists’ trust in the destination and its tourism products, reducing uncertainty and anxiety during the travel process, which is beneficial for improving the level of satisfaction (Leri and Theodoridis, 2021). Lastly, when tourists have a positive and pleasant cognitive perception of the destination image, they tend to have positive emotional experiences and leave a deep impression during the travel process. These positive emotional experiences and memories can form an emotional attachment and connection to the destination in the hearts of tourists, making them more willing to revisit.

Despite a large number of studies focusing on the impact of destination image on tourists’ decisions and satisfaction, research on how to form and change destination images in a diverse information environment is still limited. More importantly, existing studies rarely consider the interaction between different image factors and how they collectively affect tourists’ perceived value and travel intentions.

Limited Attention Theory, initially proposed by psychologist Kahneman in 1973, states that during a specific time frame, individuals can only direct their limited attentional resources towards a single object or event, making it challenging to multitask (Rochanahastin, 2020). Online attention serves as a significant informational cue that can capture tourists’ limited attentional resources, thereby influencing their travel decisions (Helmers et al., 2019). When tourists encounter a plethora of travel information, such as enticing images of destinations, positive tourist reviews, detailed travel guides, and trending topics on social media, the salience of su information makes it more likely to attract their limited attention (Shu and Tan, 2022). However, because attentional resources are finite, tourists may experience information overload in the context of excessive online information, leading to confusion in decision-making and potentially reducing travel intentions (Xiang and Cheah, 2024).

The Cognitive-Affective System Theory posits that people’s behavioral decisions are influenced not only by rational cognitive processes but also by emotional experiences (Carballo et al., 2022). In the context of tourism decision-making, tourists’ travel intentions are similarly influenced by both cognitive and affective factors (Guan et al., 2024). Cognition involves the reception, processing, and understanding of information, helping tourists evaluate and choose travel destinations (Wang et al., 2024a). Emotional experiences refers to individuals’ subjective feelings and evaluations of a destination, which can spark tourists’ desire and interest in a destination, thereby influencing their cognition and evaluation of that destination (Xu and Hu, 2024). During the travel decision-making process, tourists first use cognitive processes to analyze and evaluate travel destinations, forming a basic understanding of the destinations (Joo et al., 2023).

Based on the above analysis, this study constructs a research model as shown in Figure 1.

To ensure the scientific validity of the research data, the antecedent variable measurement items were derived from classical literature and suitably modified based on current research on tourists’ travel intentions. A ten-point Likert scale was employed, where scores from 0 to 9 represent varying degrees of agreement from “strongly disagree” to “strongly agree,” attention from “not at all” to “extremely attentive,” or frequency from “never” to “always”. Before the formal survey distribution, a pre-survey was conducted to remove items with low discriminatory power, dispersed factor loadings, and unclear meanings, resulting in the finalized scale. The scales for social attention, informational attention, cognitive image, affective image, functional value, social value, and tourists' travel intentions are shown in the  appendix.

The questionnaires were distributed randomly via online platforms such as WeChat, QQ, Weibo, Xiaohongshu, and TikTok. Various incentive measures were employed to boost participation. A total of 279 questionnaires were collected, out of which 238 were valid, yielding a response rate of 85.3%. Among the respondents, 36.1% were male and 63.9% were female, with the majority aged between 21–30 and over 50 years. The sample predominantly comprised individuals with higher education and high income, aligning with the general characteristics of the tourist population.

Qualitative Comparative Analysis (QCA) is a multifaceted method designed to explore the complexities of social science phenomena, grounded in Boolean algebra and set theory (Ragin, 2014). This method views research subjects as attribute sets and transforms research questions into the study of relationships among these attributes. Compared to traditional statistical methods, fsQCA can handle small samples and complex interactions among multiple conditions, revealing asymmetric logics (Mendel and Korjani, 2013), which is particularly important in tourism research (Rihoux, 2006). With fsQCA, we can identify how different factors work together under specific conditions to influence tourists’ decision-making processes, thus providing new perspectives and theoretical contributions to the study of tourism behavior (Kumar et al., 2024).

SPSS 26.0 software was used to test the reliability of the questionnaire scale, evaluated through Cronbach’s α coefficient. The results, as shown in Table 1, indicate that all scales have Cronbach’s α coefficients greater than 0.8, demonstrating that the scales possess high internal consistency and reliability among the subjects, with minimal measurement error, making the scales stable and reliable. Validity measures the extent to which a tool accurately measures the concept or characteristic it aims to assess, reflecting whether the tool effectively captures the concepts of interest in the study. In this study, both convergent validity and discriminant validity were used to examine the validity of the scale variables.

Regarding convergent validity, this study focuses on three indicators: Standardized Factor Loadings, Average Variance Extracted (AVE), and Composite Reliability (CR value). Standardized factor loadings ranged from 0.757 to 0.943, all significant (p < 0.05), indicating a strong association between measurement items and latent factors. An AVE greater than 0.5 and a CR value greater than 0.7 demonstrate that latent factors have a high explanatory power for observed variables and good internal consistency.

The results are shown in Table 1, where all measurement items have standardized factor loadings between 0.757 and 0.943, all greater than 0.7, with corresponding P-values less than 0.05, indicating significant positive correlations between measurement items and latent factors. Additionally, all latent factors have AVEs greater than 0.5, and CR values greater than 0.7, further validating the internal consistency and accuracy of the measurement model.

Discriminant validity analysis was conducted by comparing the square root of AVE with the correlation coefficients between latent variables to evaluate discriminant validity. If the square root of AVE is greater than the correlation coefficient, good discriminant validity is indicated. The discriminant validity analysis results in Table 2 show that the correlation coefficients between latent variables do not exceed 0.8, indicating low correlations. Each latent variable’s AVE square root exceeds the correlation coefficients with other variables, demonstrating that each variable effectively explains its measurement items and is distinctly different from other variables. Therefore, the measurement model used in this study exhibits good discriminant validity.

In the initial phase of this study, we adopted Douglas’s calibration strategy, which defines the thresholds for membership and non-membership based on the median of the conditional variable plus or minus the standard deviation, with the median serving as the crossover point. However, considering that the data originated from questionnaire scales, the scores for individual cases were all integers, as was the median, leading to ambiguity in the classification of cases with scores equal to the median. For instance, the median “a” of the conditional variable SA becomes the crossover point, and when the score “b” of a case’s conditional variable SA is exactly equal to “a”, the classification falls into a dilemma. To address this issue, we adopted Thomas’s adjustment strategy, making a slight adjustment of +0.001 for each integer anchor point, ensuring that all cases could be effectively classified and optimizing the results of the fuzzy-set analysis. For more details, see Table 3. Using the Calibrate function (x, n1, n2, n3) in fsQCA 3.0 software, the calibrated data were converted into fuzzy scores between 0 and 1, facilitating subsequent fuzzy set analysis. This adjustment enhances the accuracy and completeness of the analysis.

In the analysis of necessary conditions for high travel intention, we examined the antecedent conditions contributing to high travel intention. The results indicated that none of the antecedent conditions met the consistency threshold of 0.9. This suggests that no single factor can be identified as a necessary condition for high travel intention. Instead, the formation of high travel intention likely results from the combined influence of multiple factors, rather than the independent action of a single factor. This finding underscores the diversity and complexity of high travel intention, highlighting the need to further explore the interactions and configurational relationships among various influencing factors. The detailed results of the necessary condition analysis are presented in Table 4.

In constructing the truth table, we analyzed the six antecedent variables associated with high travel intention. Theoretically, there are 64 possible combinations. However, to ensure the credibility and effectiveness of the analysis, we applied strict conditions: Case Frequency Threshold: We set the threshold at 2, meaning only combinations that appeared at least twice were included in the analysis. Consistency Threshold: We set the consistency threshold at 0.8 to ensure high levels of consistency for the combinations obtained (Fiss, 2011). After rigorous filtering and analysis, we identified 24 valid combinations, of which 12 were associated with high travel intention.

After constructing the truth table, the next step involves standard analysis, a key procedure in fsQCA. The aim is to identify possible solutions leading to a specific outcome, which includes parsimonious solutions, intermediate solutions, and complex solutions. Parsimonious Solution: Combinations of one or a few antecedent conditions leading to high travel intention. Intermediate Solutions: Combinations of several antecedent conditions interacting to produce high travel intention. Complex Solutions: Multiple, intricate combinations and interactions among antecedent conditions leading to high travel intention. Generally, these are not included in configurational explanations due to their complexity. The results of the parsimonious and intermediate solutions for high travel intention are detailed in Tables 5 and 6 respectively.

Following Morgan (2010) proposed configurational approach to outcome expression, this study analyzed the intermediate solutions and parsimonious solutions associated with a high travel intention, utilizing specific symbols to denote the presence or absence of antecedent conditions. Antecedent conditions appearing simultaneously in both intermediate and parsimonious solutions were marked with “•” or “_⊗” to signify their status as core conditions, indicating a strong causal relationship with the outcome. Antecedent conditions appearing solely in intermediate solutions were denoted by “●” or “⊗”, indicating their status as peripheral conditions with a relatively weaker causal relationship with the outcome.

Table 7 illustrates the combination results of factors leading to a high travel intention, revealing an overall consistency of 0.856402, exceeding the required consistency threshold of 0.8, thereby indicating a high reliability of the solutions. The overall coverage reached 0.731307, suggesting that these eight configurations can account for approximately 73% of the cases, demonstrating a high explanatory power. Among the three categories of paths, H1, H2, and H3, the coverage of paths in H3 is consistently below 0.2, generally lower than that of H1 and H2. Hence, it can be inferred that the majority of travelers develop a high travel intention through H1 and H2, indicating the greater importance of these two paths. Among them, the H1 path is more persuasive in terms of coverage and consistency, with the exception of the H1b grouping, which has lower coverage, and H1a and H1c, which are more persuasive in terms of coverage and consistency.

This study identified two types of pathways that promote the travel intentions based on core conditions: cognitive-emotional image and emotional image-social value. Cognitive-Emotional image: This path is summarized from configurations H1a and H1c. Among them, the coverage (Raw coverage) of configuration H1a reaches 0.568, and the consistency (Consistency) reaches 0.907; the coverage (Raw coverage) of H1c reaches 0.535, and the consistency (Consistency) reaches 0.924. When tourists have a positive cognitive image of the destination and a strong emotional connection to it. These two factors mutually reinforce each other, providing tourists with comprehensive insights into the destination and evoking strong emotional resonance and a sense of belonging, thereby increasing their travel intentions.

Emotional image-Social Value: This path is summarized from configurations H2a and H2b. The raw coverage of configuration H2a reaches 0.201, with a consistency of 0.877; for H2b, the raw coverage is 0.215, with a consistency of 0.909. When tourists have a deep emotional connection to the destination and perceive traveling there as bringing higher social value. This combination leads tourists to value and anticipate travel more, further enhancing their motivation and interest in traveling. The synergy between emotional image and social value collectively fosters a strong inclination to travel.

Following the same procedure as the necessary condition analysis for high travel intentions, this study also examined the antecedent conditions associated with low travel intentions. Similar to the results of the necessary condition analysis for high travel intentions, none of the antecedent conditions for low travel intentions reached the consistency threshold of 0.9. This indicates that the formation of low travel intentions is not determined by a single independent factor but rather results from the interplay of multiple factors. This finding underscores the diversity and complexity inherent in the study of low travel intentions, necessitating further exploration of the interactions and configurational relationships among various influencing factors. For detailed results of the necessary condition analysis of factors influencing low travel intentions, please refer to Table 8.

In constructing the truth table, this study analyzed six antecedent variables associated with low travel intentions. Theoretically, there exist 64 possible combinations. However, to ensure the credibility and effectiveness of the analysis, stringent criteria were applied. Firstly, a case frequency threshold of 2 was set, meaning that only combinations occurring at least twice were included in the analysis. Secondly, consistency thresholds were uniformly set at 0.8 to ensure that the obtained combinations exhibited a high level of consistency. After rigorous filtering and analysis, a total of 24 combinations were obtained, with 20 combinations associated with low travel intentions.

Tables 9 and 10 respectively present the parsimonious solutions and intermediate solutions for low travel intentions, revealing different pathways leading to low travel intentions. The analysis of parsimonious solutions indicates the presence of two main pathways that may lead to the formation of low travel intentions. On the one hand, through the analysis of parsimonious solutions, this study can quickly identify factor combinations significantly impacting low travel intentions, providing important clues for subsequent research. On the other hand, the analysis of intermediate solutions is more comprehensive, revealing eight different pathways. These pathways encompass a greater variety of antecedent condition combinations, reflecting the complexity and diversity in the formation of low travel intentions.

Drawing on Morgan’s configurational research approach, this study further optimized the expression of results when analyzing the configurational results leading to low travel intentions (Morgan, 2010), as detailed in Table 11. Based on the different core conditions, the eight configurations were classified into three categories: L1, L2, and L3. In these categories, L1 contains two core conditions, ∼SA and ∼CI, with one pathway (L1a); L2 contains ∼ CI as the core condition, with four pathways (L2a, L2b, L2c, L2d); and L3 contains ∼ SA as the core condition, with three pathways (L3a, L3b, L3c).

As shown in Table 11, the overall consistency of the eight pathways obtained in this study is 0.813994, exceeding the consistency threshold of 0.8, indicating high reliability of the results. Moreover, the overall coverage reached 0.73488, suggesting that the results of this study can explain approximately 73% of the cases leading to low travel intentions. Among these eight pathways, L1a and L3c have a coverage of 0.520, higher than other pathways. Although the coverage of L3c is relatively high, the coverage of L3a and L3b is insufficient, indicating that compared to L3, situations where low travel intentions is generated through L1 are more common, making this pathway more important.

This study identified eight combinations of factors that can lead travelers to develop low travel intentions. Among these eight combinations, the core condition present in all of them is ∼social concern * ∼cognitive image, with a raw coverage of 0.52 and a consistency of 0.920. Therefore, based on this core condition, this study classified all configurational results into one type, namely the social concern-cognitive image type. When travelers perceive low social concern, they may reduce their travel intentions due to a lack of motivation for sharing and recognition. Hence, a lack of social concern can diminish the motivation to travel. Low social attention not only affects the motivation to travel but also further reduces the intention to travel by weakening tourists' positive cognitive image of the destination.

The cognitive image of the destination is a key factor influencing travel intentions. If travelers have a generally negative view and assessment of the destination, they might opt to forgo the trip. Negative cognitive image can significantly reduce the willingness to travel, especially when the destination lacks attractiveness or has environmental issues. Furthermore, although emotional image and functional value are considered peripheral conditions, they also have a certain impact on travel intentions. When travelers have weak or negative emotional connections to the destination, and perceive that the destination’s functional value is insufficient to meet their needs, their travel intentions will correspondingly decrease.

This study found that the interaction between cognitive image and emotional image is a significant factor in driving tourists' travel intentions. This discovery contradicts the traditional belief that cognitive and emotional images have separate impacts, as previously thought (Ghaderi et al., 2019). Instead, it suggests that these images work together to influence outcomes. Specifically, it posits that the multidimensionality of destination image determines its overall impact on tourists’ travel intentions. Positive cognitive image leads tourists to favorably evaluate the natural scenery, cultural heritage, and service quality of the destination, while the emotional image arises from the emotional connection and fond memories tourists associate with the destination. When these two factors interact, they mutually reinforce each other, forming a comprehensive understanding of the destination and significantly enhancing tourists’ travel intentions.

The combination of emotional image and social value stimulates travel motivation. When tourists develop a strong emotional connection to a destination and recognize that travel can not only fulfill their personal emotional needs but also enhance their self-image and strengthen their social relationships, their travel intentions increase significantly. This finding expands existing research horizons (Zhang et al., 2022) while providing new strategic insights into tourism marketing. Tourism enterprises should not only showcase the natural and cultural features of the destination but also skillfully integrate emotional elements and social value into their promotions, thereby creating a highly attractive travel brand that stimulates potential tourists' travel motivations.

This study reveals the mechanisms by which social attention and cognitive image suppress low travel intentions, addressing the gap in previous research that has primarily focused on factors that enhance travel intentions (Bianchi, 2022). When social attention is low or the cognitive image is poor, tourists' travel intentions decrease significantly. This finding validates Dash et al. (2021)’s research on the impact of social media on travel decision-making, uncovering the interaction between social attention and cognitive image. Therefore, tourism enterprises and organizations should also avoid these negative factors when promoting travel intentions in order to increase the social attention and cognitive image of tourists.

This study contributes to deepening the understanding of the mechanism of tourist behavior decision-making and expanding theoretical frameworks. This study delves into the impact of online attention, destination image, and perceived value on tourists' travel intentions, thereby deepening the understanding of decision-making mechanisms in tourism behavior and extending the application of limited attention theory and the “cognitive-affective” system theory. Hou et al. (2019) found that social media and online reviews have a profound impact on tourists' travel choices. This study further uncovers the complex pathways through which online communication affects travel decisions, thereby enriching the conceptual scope of limited attention theory. Additionally, this study tests whether the destination image, as an overall perception and impression of a destination by tourists, significantly influences travel intentions. By revealing the interaction mechanisms within this system, the research advances the application of the “cognitive-affective” system theory and deepens the understanding of its multidimensionality and complexity in travel decision-making.

Expanding the application fields of research methods, this study employs the configurational analysis method based on fsQCA, not only breaks through the limitations of traditional statistical analysis in dealing with complex relationships but also expands the application of research methods in the field of tourism. The fsQCA method can reveal the impact of combinations of multiple factors on outcomes, suitable for studying the interactions of multiple factors in tourist behavior. Moreover, the findings of this study hold significant theoretical value for the fields of marketing, consumer behavior, and organizational management. These areas also encounter the interplay of complex factors, and the fsQCA method can assist researchers in gaining deeper insights into the formation mechanisms of complex decision-making processes (Geremew et al., 2024). Therefore, this study provides a beneficial example and reference for the expansion of the application fields of research methods.

Tourism destinations and organizations need to deeply understand and strengthen their brand image and emotional connection with tourists. Positive cognitive image is the primary factor attracting tourists, covering various aspects such as natural scenery, cultural characteristics, and service quality. For instance, Lijiang in Yunnan has successfully created a distinctive tourism brand by promoting the “Naxi Culture” and integrating natural landscapes with local ethnic minority culture, attracting a large number of tourists. Personalized services and special activities are conducive to establishing emotional connections, allowing tourists to feel a sense of belonging and happiness during their travels, thereby increasing their loyalty and satisfaction.

The tourism industry should deeply explore and emphasize the social value of travel. For example, rural tourism projects in Guizhou, China, encourage tourists to experience traditional farming culture and participate in local handicraft making. This not only enriches the cultural experience for tourists but also helps increase the income of local residents, thereby promoting rural revitalization. Tourism enterprises should actively promote these values to attract more tourists who value quality of life and cultural pursuits. At the same time, tourism enterprises should cooperate with local governments and communities to promote sustainable development of tourism, including participating in local environmental projects, supporting cultural heritage preservation, and providing employment opportunities for local residents.

Strengthening media cooperation and driving technological innovation. By strengthening collaboration with media outlets, increasing the visibility of tourism destinations, and leveraging social media and other new media platforms, tourism enterprises can actively conduct online promotional activities. By leveraging technologies such as big data, artificial intelligence, and virtual reality (VR), the tourism industry can enhance the intelligence of its services, offering tourists more convenient and personalized experiences. For example, the VR experience at the Forbidden City Museum in China enables visitors to “tour” the site before going to their destination, which enhances their interest in traveling.

This study also has certain limitations. Firstly, the data predominantly stem from questionnaire surveys, which may be susceptible to respondent bias. In future research, it is advisable to obtain more diverse and comprehensive data to enhance the generalizability of the findings. Secondly, the scope of the sample is relatively limited, failing to adequately consider the behavioral differences among tourists from various regions and cultural backgrounds. Expanding the research scope in the future could reveal broader mechanisms underlying the formation of travel intentions. Lastly, this study focuses on the impact of cognitive and affective images on travel intentions, without taking into account other potential influencing factors. Future studies could further consider the combined effects of personal economic conditions and time constraints, among other factors, to enrich the research conclusions.

Social Attention (SA) (Simonetti and Bigne, 2022; Ye et al., 2021).

• SA1: Before traveling, do you pay attention to the official social media of the destination?

• SA2: Before traveling, have you ever participated in discussions or left comments related to the destination?

• SA3: Before traveling, do you interact with travel-related content on social media?

• SA4: Before traveling, have you ever shared content about the travel destination?

• SA5: Before traveling, do you obtain travel experiences from other tourists via social media?

• SA6: How much does social media influence your travel decisions before traveling?

Informational Attention (IA) (Chen et al., 2024; Xi et al., 2021).

• IA1: Before traveling, do you frequently search for relevant information online?

• IA2: How important is online travel information to you before traveling?

• IA3: Before traveling, do you trust the travel information obtained online?

• IA4: Before traveling, does online information influence your adjustments to travel plans?

• IA5: What are your requirements for the accuracy and completeness of travel information before traveling?

Cognitive Image (CI) (Huang and Lin, 2024; Vazquez et al., 2023).

• CI1: Overall, how well do you understand the geography and attractions of the travel destination?

• CI2: Overall, to what extent are you familiar with the history and culture of the travel destination?

• CI3: Overall, how satisfied are you with the environment and facilities of the travel destination?

• CI4: Overall, how well do you understand the services provided by the travel destination?

• CI5: Overall, how well do you understand the convenience of the travel destination?

• CI6: Overall, how satisfied are you with the safety of the travel destination?

Emotional image(EI) (Chen and Peng, 2023; Wang et al., 2023).

• EI1: Do you feel a sense of familiarity with the travel destination?

• EI2: Does thinking about the travel destination make you feel relaxed, joyful, or excited?

• EI3: Do you believe the travel destination will leave a lasting impression on you?

• EI4: Do you think the travel destination will bring you satisfaction?

• EI5: Are you willing to recommend this travel destination to friends and family?

Functional Value(FV) (Lam et al., 2020; Yang et al., 2023).

• FV1: Do the products and services at the travel destination meet your needs?

• FV2: Are you satisfied with the quality of the products and services at the travel destination?

• FV3: Are the products and services at the travel destination efficient and practical?

• FV4: How is the cost-effectiveness of the products and services at the travel destination?

• FV5: Do the products and services at the travel destination solve your problems?

Social Value (SV) (Chae et al., 2020; Wang et al., 2024b).

• SV1: Can traveling to this destination enhance your personal image?

• SV2: Can traveling to this destination help you make more friends?

• SV3: Can traveling to this destination help you leave a good impression on others?

• SV4: Can traveling to this destination give you a sense of belonging?

• SV5: Can traveling to this destination bring you some social approval?

Travel Intention (TI) (Liu et al., 2023; Zheng et al., 2023).

• TI1: How much are you looking forward to traveling to this destination?

• TI2: How likely are you to travel to this destination if conditions permit?

• TI3: If given the opportunity, are you willing to spread positive news about this destination?

• TI4: In your opinion, is this travel destination worth your time and effort?

• TI5: Does traveling to this destination meet your current travel needs?