Sage Journals: Discover world-class research

Abstract

In the digital era, tourists are surrounded by online reviews, social word-of-mouth and destination branding that shape their expectations long before a trip begins. Yet it remains unclear how these digital information sources and brand perceptions are translated into tourist satisfaction through on-site tourism experiences. Based on expectation–confirmation theory, this study examines the relationships among online reviews, word-of-mouth, brand image, tourism experience and tourist satisfaction with a particular focus on the mediating role of tourism experience. A total of 517 valid responses were collected through a structured questionnaire from visitors to major tourist attractions in Henan Province, China. Data were analyzed using structural equation modeling (SEM). The results show that online reviews, word-of-mouth, and brand image have significant positive effects on tourist satisfaction. Tourism experience plays a partial mediating role in all three paths, strengthening the relationship between external perceptions and satisfaction. These findings provide both theoretical insights and practical implications for tourism destination managers. Specifically, enhancing the quality of tourism experiences and cultivating a positive brand image can effectively boost tourist satisfaction in the digital age.

Keywords

online reviews word-of-mouth brand image tourism experience tourist satisfaction

Introduction

Tourist satisfaction, as a core variable in tourism research, refers to tourists’ emotional state or overall evaluation of the entire travel process while enjoying the tourism experience, such as their satisfaction with tourism products and services (Oliver, 2010). With the rapid development of the global tourism industry, tourist satisfaction is not only related to individuals’ travel experiences but also to the maintenance of destination reputation, the stimulation of revisit intentions, and the achievement of sustainable industry development (Hoang et al., 2024; Miah et al., 2025; Nian et al., 2024). As one of the most populous countries in the world, China has a large and diverse tourism market, with continuously growing domestic demand and increasingly sophisticated tourism consumption. This situation places higher demands on the refined management and high-quality supply of tourism services (Gao et al., 2024; Nian et al., 2025). According to the latest Economic Impact Research published by the World Travel and Tourism Council (2025), China is emerging as one of the most dynamic travel and tourism markets worldwide. Against this backdrop, tourist satisfaction has become a focal concern for government agencies, tourism enterprises, and the academic community. Therefore, uncovering the formation mechanisms of tourist satisfaction has become a key issue in tourism research and destination management.

Research indicates that the formation of tourist satisfaction is not determined by a single factor but rather results from a complex psychological evaluation process shaped by the interaction of tourist perceptions, destination attributes, and tourism experiences (Arranz Val et al., 2024; Ho et al., 2024; X. Sun et al., 2024). Prior to travel, tourists often rely on digital informational resources including online reviews, word-of-mouth (WOM), and brand image (BI) to form initial perceptions of a destination. These perceptual cues not only influence expectation setting but also have a significant impact on post-travel satisfaction evaluations (Contu et al., 2024; Limonta et al., 2024; Lin et al., 2024; Nguyen et al., 2022). Nguyen et al. (2022) emphasize that online reviews, by providing abundant user-generated content, help reduce perceived risks, whereas WOM enhances credibility through interpersonal recommendations (Lin et al., 2024). BI reflects tourists’ overall identification with and emotional attachment to a destination (Jagani & Saboori-Deilami, 2025). Furthermore, tourism experience, as the actual contact and sensations tourists have at the destination, serves as a crucial bridge linking perceptions to satisfaction (Zhou et al., 2025). In this context, examining how online perception factors shape tourist satisfaction can deepen understanding of tourist psychology and behavior while also guiding improvements in destination services and communication.

In recent years, studies on tourist satisfaction have expanded considerably, generating extensive empirical evidence, particularly on the relationship between tourism experience and behavioral intentions (Pratminingsih et al., 2025; Su, 2025). However, gaps remain at both the theoretical and mechanistic levels. First, most studies focus on external factors influencing satisfaction, with limited attention to the formation of tourists’ expectations prior to travel and the confirmation process after travel. In particular, the crucial psychological pathway of “expectation–confirmation–satisfaction” has been insufficiently examined, especially regarding the mechanisms through which perceived information is transformed into final satisfaction evaluations. Second, with the development of digital media, tourists are increasingly relying on perceived information such as online reviews, WOM, and BI when making travel decisions. As key sources of expectations, these forms of information not only shape tourists’ psychological anticipation of a destination but may also indirectly affect satisfaction through the confirmation or disconfirmation of actual tourism experiences. Nevertheless, existing research often treats these perception-related variables as independent predictors, lacking integrative analyses and failing to adequately uncover their roles in the expectation–confirmation process. Against this backdrop, this study adopts Expectation–Confirmation Theory (ECT) to develop a framework linking online reviews, WOM, and BI to tourist satisfaction through tourism experience. By clarifying how digital perception-related information is transformed into satisfaction evaluations, this study extends the application of ECT in the digital tourism context and provides implications for destination branding and service management.

Literature Review and Hypotheses

Expectation-Confirmation Theory

This study adopts ECT (Oliver, 1980) as its theoretical foundation to explain how tourists’ perceptions shaped by digital information are transformed into satisfaction through actual tourism experiences. According to ECT, satisfaction results from individuals’ cognitive assessment of how perceived performance aligns with their prior expectations (Cao et al., 2025; Yuan & Marzuki, 2024). Within digital tourism, online reviews, WOM, and BI serve as the primary external antecedents of expectation formation (Aboalganam et al., 2025; Chen & Kim, 2025). Online reviews help tourists form cognitive expectations of service quality through abundant user-generated content (Nawawi et al., 2024), while WOM strengthens affective expectations through interpersonal trust (Vidosavljević & Đurić, 2025). BI, as a long-term and stable cognitive framework, reinforces tourists’ overall expectations through value identification and emotional attachment (Jimenez-García et al., 2025; Tahir et al., 2024). During the actual trip, these expectations are either confirmed or disconfirmed through tourism experience. Tourism experience represents the critical stage where expectations interact with reality, encompassing tourists’ sensory perceptions, emotional responses, and cognitive evaluations (Cao et al., 2025; Seow et al., 2024). When the actual experience meets or exceeds expectations, positive confirmation occurs, leading to greater satisfaction (Gao et al., 2025). By positioning tourism experience as a mediating mechanism, this study integrates digital perception factors and satisfaction formation within a unified ECT framework, revealing how external information is internalized and transformed into evaluative judgments in the digital tourism context.

Online Reviews and Tourist Satisfaction

Online reviews are user-generated product-related information provided by tourists based on their personal experiences (De Pelsmacker et al., 2018). Recent studies have revealed that the content quality and emotional tone of online reviews significantly influence tourists’ expectation formation and post-experience evaluations, especially within digital tourism (Aboalganam et al., 2025; George & Ramos, 2024). Tourists hold high expectations based on online reviews, and when their actual experiences match or surpass these expectations, satisfaction levels rise markedly (Kang et al., 2022; Pooja & Upadhyaya, 2025). For instance, Kang et al. (2022) emphasize that the credibility, richness, and authority of information in online reviews play a critical role in shaping tourist satisfaction. Shi et al. (2025) further revealed that online reviews not only reduce tourists’ perceived uncertainty in the destination selection process but also enhance their decision confidence and emotional assurance, thereby strengthening overall satisfaction. According to ECT (Oliver, 1980), external informational cues, such as online reviews, shape tourists’ expectations prior to travel and generate confirmation or disconfirmation when actual experiences are compared with those expectations, thereby directly influencing the level of satisfaction. Therefore, this study proposes the following hypotheses:

H1: Online reviews have a significant positive effect on tourist satisfaction.

Word-of-Mouth and Tourist Satisfaction

WOM refers to informal communication between private parties concerning evaluations of or information about a target object (Anderson, 1998). Rapidly disseminated through social media and digital platforms, WOM enables tourists to access authentic evaluations of destinations, directly influencing their choices and satisfaction (Aravindan et al., 2023; Pham et al., 2025; Xu et al., 2023). Liu et al. (2025) pointed out that positive WOM enhances tourists’ trust and emotional attachment to a destination, forming positive expectations and increasing satisfaction. In contrast, negative WOM reduces perceived credibility and triggers psychological resistance, leading to more stringent subjective evaluations that lower satisfaction (H. Sun et al., 2024). As a widely disseminated type of information, WOM plays a critical role in shaping tourist satisfaction, significantly influencing destination choice. Within the framework of ECT (Oliver, 1980), the distinct feature of WOM lies in its reliance on the credibility of the communicator and the strength of interpersonal relationships. Such interpersonal trust is more likely to elicit affective expectations, which, during the confirmation process of actual experiences, often exert a more pronounced influence on changes in satisfaction. Therefore, this study proposes the following hypotheses:

H2: WOM has a significant positive impact on tourist satisfaction.

Brand Image and Tourist Satisfaction

BI has become a crucial component of a brand; it refers to the ideas, beliefs, or perceptions associated with an entity (Kotler, 2004). Through distinctive characteristics in the market, perceived value, and brand associations, BI fosters tourists’ trust in a destination, thereby enhancing the quality of their tourism experience and significantly increasing satisfaction (Agapito & Sigala, 2024; Jimenez-García et al., 2025; Kumar et al., 2024; Tahir et al., 2024). Agapito and Sigala (2024) revealed that satisfaction rises significantly when the actual experience meets or exceeds expectations set by BI. Chiwaridzo and Chiwaridzo (2024) further pointed out that the formation of BI depends not only on visual and symbolic recognition but also on maintaining consistent value expression across multiple communication channels. This consistency strengthens tourists’ trust and satisfaction. Within the framework of ECT (Oliver, 1980), BI, as a long-term and stable cognitive framework, not only helps tourists establish overall expectations prior to travel but also shapes their anticipated evaluations of the destination through value identification and emotional attachment. The consistency between these expectations and actual experiences largely determines the level of satisfaction. Therefore, this study proposes the following hypotheses:

H3: BI has a significant positive impact on tourist satisfaction.

Tourism Experience and Tourist Satisfaction

Tourism experience refers to the overall perceptions and meaning construction that tourists acquire during the travel process through multiple dimensions, including sensory, emotional, cognitive, and behavioral aspects (Larsen, 2007). Previous studies have found that tourists’ tourism experience is significantly positively correlated with their satisfaction (Liu et al., 2022; Su et al., 2023; Tešin et al., 2025). Su et al. (2023) found that satisfaction significantly increases when tourists’ actual experiences align with their expectations. Meanwhile, Zhou and Wang (2024) argued that a high-quality tourism experience is reflected not only in material or functional satisfaction but also in the psychological engagement and emotional resonance that generate feelings of joy and achievement. Sthapit et al. (2025) further found that the influence of tourism experience on satisfaction is enduring, as it strengthens tourists’ immediate positive emotional responses and extends to their future behavioral intentions, including intentions to revisit the destination and recommend it to others. Poor experiences can lead to a significant drop in satisfaction, even affecting future travel choices (Ye et al., 2021). Therefore, this study proposes the following hypotheses:

H4: Tourism experience has a significant positive impact on tourist satisfaction.

The Mediating Role of Tourism Experience

Online reviews serve not only as an essential source of tourism information but also indirectly influence tourist satisfaction by shaping the tourism experience (Kang et al., 2022; Sun et al., 2023; X. Sun et al., 2024). Before traveling, tourists draw on online reviews to construct mental representations of destinations, thereby forming expectations about service quality and experiential authenticity (Islam et al., 2025). High-quality and credible reviews help reduce uncertainty and enhance tourists’ perceived control, encouraging more active participation in various activities during their trips (Yadav et al., 2023). Moreover, Zhou and Wang (2024) reported that emotionally rich and experience-based reviews can evoke tourists’ anticipatory emotions and promote deeper psychological immersion during their actual travel experiences. When tourists’ real experiences align with the contexts and emotions conveyed in online reviews, this perceived congruence strengthens their positive experiences and enhances overall satisfaction (Guerreiro et al., 2025). Therefore, by influencing the tourism experience, online reviews become a key factor in enhancing tourist satisfaction.

As another important source of tourism information, WOM also plays a significant role in shaping tourists’ tourism experiences (Liu et al., 2025; Ribeiro & Kalro, 2023; Schoner-Schatz et al., 2021). Schoner-Schatz et al. (2021) found that positive WOM, such as recommendations from friends and family or authentic feedback from previous tourists, significantly enhances tourists’ trust in the destination, leading to higher expectations before their visit. This positive WOM information improves tourists’ actual experiences, further boosting their satisfaction (Su et al., 2023). Conversely, negative WOM reduces perceived credibility and heightens risk awareness, which not only lowers pre-trip expectations but may also trigger more critical subjective evaluations and negative interpretations during the experience (Pham et al., 2025).

BI, as the product of long-term market positioning and communication, not only builds an overall expectation for tourists before travel but also shapes their travel experiences through value identification and emotional connection (Ho et al., 2024; Mohamed & Ünsalan, 2025; Sukaatmadja et al., 2023). Sukaatmadja et al. (2023) emphasized that a strong BI not only provides tourists with a sense of security and trust but also raises their psychological expectations before travel, fostering positive anticipation toward the destination. Moreover, Ho et al. (2024) found that when the BI aligns with the actual experience, the congruence between expectation and reality significantly boosts satisfaction, making tourists more receptive to and appreciative of the destination’s services and facilities. BI shapes tourists’ travel experiences by providing clear value propositions and service promises that guide their attention and expectation scripts, enhancing the perceived authenticity and emotional security of on-site experiences, and thereby improving the overall quality of their travel experience (Wu, 2025). According to ECT (Oliver, 1980), tourists form expectations based on external information prior to travel, and during the actual tourism process, they confirm or disconfirm these expectations through their experiences. Therefore, tourism experience plays a critical mediating role between external information and satisfaction. Therefore, this study proposes the following hypotheses:

H5: Tourism experience mediates the impact of online reviews on tourist satisfaction.

H6: Tourism experience mediates the impact of WOM on tourist satisfaction.

H7: Tourism experience mediates the impact of BI on tourist satisfaction.

Based on the above hypotheses, the research model of this study is proposed, as shown in Figure 1.

Figure 1.

Theoretical hypothesis model.

Methods

Research Design

This study adopted a quantitative cross-sectional design to investigate the relationships among online reviews, WOM, BI, tourism experience, and tourist satisfaction. Data were collected through a structured questionnaire administered to tourists at several key scenic areas in Henan Province, China. The participants covered a wide range of ages, occupations, and places of residence, ensuring sample heterogeneity and generalizability. The research model was constructed based on prior theoretical frameworks and empirical findings, with a specific focus on how perceived online factors (i.e., online reviews, WOM, BI) affect tourist satisfaction indirectly through the mediating role of tourism experience.

Data Collection

This study utilized a random sampling approach for questionnaire distribution and data collection, with data gathered from May to November 2024. The sample selection process was as follows: first, five popular tourist attractions in Henan Province, including Shaolin Temple Scenic Area, Longmen Grottoes, and Yuntai Mountain Scenic Area, as well as Qingming Riverside Landscape Garden (Millennium City Park) and Guoliang Village Scenic Area (known for their significant cultural and natural heritage), were randomly selected as sample sources. Kaifeng, being a recent tourism hotspot, received particular focus. Additionally, popular attractions in Luoyang and Zhengzhou were included to ensure regional representation. Then, according to the visitor registration information on the official website of each scenic spot, the samples were randomly selected.

The questionnaire was distributed via the Wenjuanxing platform (https://www.wjx.cn/) and shared with target tourists through social media, email, and tourism platform links to obtain a broader sample. All participants provided informed consent before completing the questionnaire. According to Kline (2018) guideline for sample size estimation, at least 10 respondents are required per questionnaire item. This study comprised 26 items; allowing for an anticipated attrition rate of approximately 20%, the minimum required sample size was determined to be 312. A total of 550 questionnaires were distributed, and 531 were returned, yielding a response rate of 96.5%. The exclusion criteria were as follows: (1) incomplete responses (e.g., missing answers or an entire section left unanswered); and (2) patterned or careless responding, including selecting the same response option throughout the questionnaire or exhibiting invariant response patterns (Meade & Craig, 2012). After removing incomplete and careless responses, 517 valid questionnaires were retained for analysis, with 320 male respondents (61.9%) and 197 female respondents (38.1%; Bo, 2025). Table 1 presents the basic demographic characteristics of the tourists and the distribution of satisfaction levels.

Table 1.

Demographic Characteristics.

Variables	Category	Quantity	Percentage (%)
Gender	Male	320	61.9
Gender	Female	197	38.1
Age	Under 18 years old	4	.77
	19–30 years old	313	60.54
	31–45 years old	134	25.92
	46–60 years old	51	9.86
	Over 60 years old	15	2.9
Area of residence	Cities	232	44.87
Area of residence	Rural/suburban areas	285	55.13
The only child	Yes	46	8.9
The only child	No	471	91.1
Type of occupation	Student	164	31.72
	Office worker	249	48.16
	Freelancers	59	11.41
	Business owner/manager	45	8.7

Measurement Tools

The survey is structured into two sections: collects information from participants and evaluate the constructs relevant to this study. Established and validated scales were used to measure each of the research’s constructs. Apart from demographic factors, variable was assessed using a five-point Likert scale.

Online Reviews Scale

This study utilized an online reviews scale (ORS) to assess the impact of online reviews on tourists. The scale was adapted from the ORS designed by Liu and Meng (2024) and includes 10 items, such as “Online reviews of the destination are authoritative.” Higher scores reflecting a better perception of online reviews of the location. The Cronbach’s alpha is .940, indicating high internal consistency.

Word-of-Mouth Scale

Drawing on the framework established by Seow et al. (2024), this research employed five indicators to assess how WOM influences tourists’ decisions regarding their destination selection. These items include: “Knowing that such a destination leaves a good impression on others,”“Ensuring that the destination I choose is worth visiting,”“Regularly gathering information about destinations from others,”“Overcoming my concerns about choosing a destination,” and “Feeling confident about visiting the destination.” Higher scores on the scale indicate a more favorable WOM perception of the destination. The Cronbach’s alpha is .908, indicating high internal consistency.

Brand Image Scale

This study employed the brand image scale (BIS) developed Nie and Zeng (2024) to assess the impact of BI on tourists. The scale includes four items, such as “This tourist destination is very attractive.” Higher scores reflecting a more favorable evaluation of the destination’s BI by tourists. The Cronbach’s alpha is .916, indicating strong internal consistency.

Tourism Experience Scale

The measurement of tourism experience in this study primarily references the scale by Hung et al. (2021), which includes four items. Tourists were asked about their tourism experience, including statements such as “This trip helped me enhance my confidence,”“This trip helped me build a sense of personal identity,”“This trip allowed me to understand myself better,” and “This trip helped me learn new skills.” Higher scores in this study indicate a better tourism experience. The Cronbach’s alpha is .881, demonstrating high internal consistency.

Tourist Satisfaction Scale

This study used a tourist satisfaction scale (TSS) to assess tourists’ satisfaction with the destination, derived from the scale by Jebbouri et al. (2021). It includes three items, such as “I am happy with my decision to visit this place.” Elevated scores correspond to greater levels of tourist satisfaction. The Cronbach’s alpha is .865, demonstrating high internal consistency.

Statistical Analysis

A multi-stage analytical approach was applied to test the proposed research model. First, descriptive statistics and Pearson correlation analysis were conducted to understand the data distribution and preliminary relationships among variables. Second, PLS-SEM was used due to its suitability for predictive analysis, handling non-normal data, and supporting complex models with latent variables and mediation paths (Shmueli et al., 2019). Third, the measurement model was assessed for internal consistency and validity. Reliability was evaluated using Cronbach’s alpha and composite reliability (CR), while convergent validity was assessed via average variance extracted (AVE; Henseler et al., 2015). Discriminant validity was examined using the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio (HTMT; Brown, 2002). Fourth, the structural model was evaluated by examining multicollinearity (variance inflation factors, VIF; Hair et al., 2021), explained variance (R²), and predictive relevance (Q²), providing insight into the model’s explanatory and predictive power (Hair et al., 2021). Finally, a bootstrapping procedure with 5,000 resamples was used to assess the significance of path coefficients and test the mediating effect of tourism experience in the relationships between OR, WOM, BI, and tourist satisfaction (Preacher & Hayes, 2008). This comprehensive procedure ensured the robustness and credibility of the measurement tools and structural relationships proposed in the study.

Results

Descriptive Analysis

This study examined the relationships among online reviews, WOM, BI, tourism experience, and tourist satisfaction. As shown in Table 2, online reviews (r = .741, p < .001), WOM (r = .705, p < .001), BI (r = .731, p < .001), and tourism experience (r = .735, p < .001) were all significantly positively correlated with tourist satisfaction.

Table 2.

Mean Value, Standard Deviation and Correlation Analysis (r) of Each Variable.

Variables	M ± SD	OR	WOM	BI	TE	TS
OR	37.48 ± 10.26	1
WOM	19.51 ± 5.36	.733***	1
BI	15.71 ± 4.32	.733***	.758***	1
TE	14.49 ± 4.43	.626***	.603***	.620***	1
TS	11.68 ± 3.04	.741***	.705***	.731***	.735***	1

Note. OR = online reviews; TE = tourism experience; TS = tourist satisfaction.

***

p < .001.

Measurement Model

Table 3 presents the factor loadings and reliability results, showing that all AVE values exceeded the threshold of .5 (Henseler et al., 2015) and that all measured items and constructs demonstrated good internal consistency. In addition, all HTMT values were below the acceptable threshold of .85 (Brown, 2002), and the square roots of the AVE values were greater than the correlations between the corresponding constructs and other constructs (Tables 4 and 5). The measurement model in this study demonstrates good validity.

Table 3.

Reliability and Validity of the Model.

Constructs	Items	Loadings	CR	Cronbach’s α	AVE
OR	OR1	.825	.949	.940	.650
	OR2	.845
	OR3	.831
	OR4	.844
	OR5	.806
	OR6	.836
	OR7	.835
	OR8	.740
	OR9	.733
	OR10	.753
WOM	WOM1	.844	.932	.908	.732
	WOM2	.832
	WOM3	.876
	WOM4	.849
	WOM5	.876
BI	BI1	.902	.941	.916	.799
	BI2	.890
	BI3	.888
	BI4	.895
TE	TE1	.879	.918	.881	.737
	TE2	.868
	TE3	.889
	TE4	.794
TS	TS1	.898	.917	.865	.787
	TS2	.886
	TS3	.877

Table 4.

Fornell–Larcker Criterion.

Variables	WOM	BI	OR	TE	TS
WOM	.856
BI	.759	.894
OR	.735	.735	.806
TE	.601	.618	.629	.858
TS	.705	.731	.742	.749	.887

Note. The square roots of AVE values are shown in bold on the diagonal of the table.

Table 5.

Heterotrait–Monotrait Criterion.

Variables	WOM	BI	OR	TE
WOM
BI	.832
OR	.794	.790
TE	.673	.689	.689
TS	.796	.820	.822	.848

Note. OR Rtonline reviews; TE = tourism experience; TS = tourist satisfaction.

Structural Model

Collinearity Test

The VIF values for the underlying constructs were first evaluated in order to evaluate multicollinearity within the model (Hair et al., 2021). The fact that all of the latent constructs’ VIF values fell below the crucial 3.3 threshold indicates that multicollinearity is not an issue (Table 6).

Table 6.

Collinearity Test of the Structural Model.

Variables	TE	TS
WOM	2.811	2.872
BI	2.816	2.933
OR	2.592	2.770
TE		1.852
TS

Note. OR = online reviews; TE = tourism experience; TS = tourist satisfaction.

Path Hypothesis Test

Table 7 and Figure 2 indicates that all hypotheses are supported. Among the key predictors of tourist satisfaction, tourism experience (β = .383, t = 7.326, p = .000) emerges as the most influential factor, succeeded by online reviews (β = 0.249, t = 4.805, p = .000), BI (β = .212, t = 3.687, p = .000), and WOM (β = .131, t = 2.734, p = .006). Similarly, among the factors influencing tourism experience, online reviews (β = .310, t = 3.687, p = .000) have the greatest impact, succeeded by BI (β = .252, t = 3.975, p = .000) and WOM (β = .182, t = 3.004, p = .003).

Table 7.

Test Results of Path Hypothesis.

Path	β	2.50%	97.50%	t	p	Results
WOM→TE	.182	.066	.303	3.004	.003	Supported
WOM→TS	.131	.040	.226	2.734	.006	Supported
BI→TE	.252	.135	.385	3.975	.000	Supported
BI→TS	.212	.108	.334	3.687	.000	Supported
OR→TE	.310	.192	.433	5.053	.000	Supported
OR→TS	.249	.154	.358	4.805	.000	Supported
TE→TS	.383	.292	.496	7.326	.000	Supported

Note. OR = online reviews; TE = tourism experience; TS = tourist satisfaction.

Figure 2.

Path coefficient.

Model Interpretability

Finally, as presented in Table 8, the R² value for tourist satisfaction suggests that its predictors explain 71.9% of the total variance in tourist satisfaction. Likewise, the R² value for tourism experience shows that its predictors account for approximately 46.0% of its total variance. Additionally, all Q² values exceed 0, suggesting that the empirical model demonstrates strong predictive capability.

Table 8.

Explanatory Power of the Model.

Variables	R ²	R ² (Adjusted)	Q ²
TE	.460	.457	.336
TS	.719	.717	.559

Note. TE Ettourism experience; TS = tourist satisfaction.

Analysis of Mediating Effect

To assess the mediating role of tourism experience in the connections among online reviews, WOM, BI, and tourist satisfaction, a mediation analysis was conducted. Table 9 reveal that tourism experience serves as a mediator in the effect of online reviews, WOM, and BI on tourist satisfaction.

Table 9.

Mediation Analysis.

Relationship	Indirect effect	t	p	Direct effect	t	p	Mediation type
WOM→TE→TS	.070	2.842	.005	.131	2.734	.006	CPM
BI→TE→TS	.096	3.408	.001	.212	3.687	.000	CPM
OR→TE→TS	.119	3.866	.000	.249	4.805	.000	CPM

Note. OR = online reviews; TE = tourism experience; TS = tourist satisfaction; CPM = complementary partial mediation.

Discussion

This research thoroughly explored the impact of online reviews, WOM, BI, and tourism experience on tourist satisfaction. The findings suggest that online reviews, WOM and BI can not only directly enhance tourist satisfaction but also further strengthen this effect by improving the tourism experience. The following section provides an analysis of the study’s results.

Online reviews has a significant positive effect on tourist satisfaction, which aligns with the results of Zhang et al. (2022). Online reviews not only help tourists develop deeper insight into the characteristics and credibility of a destination before traveling but also effectively raise their expectations, which positively influences their satisfaction (Pooja & Upadhyaya, 2025). Through online reviews, tourists are able to make more confident decisions about destinations and services, reducing uncertainty in their choices and boosting trust in the destination (George & Ramos, 2024). Positive reviews increase the destination’s appeal, while negative reviews may deter potential tourists (Farias et al., 2022). For tourism businesses, managing online reviews content and responding to tourist feedback is essential.

WOM exerts a significant positive effect on tourist satisfaction, consistent with the results of Arumugam et al. (2023). WOM not only provides tourists with more authentic evaluations when selecting a destination but also offers insights into other tourists’ experiences and feedback, enabling potential visitors to more accurately assess whether the destination meets their needs (Stanovčić et al., 2021). Positive WOM enhances tourists’ trust in the destination and their expected outcomes, thereby increasing overall satisfaction (Liu et al., 2025). Additionally, tourism practitioners can collect and analyze this feedback to identify service shortcomings and continually improve service quality, leading to increased satisfaction and loyalty over the long term (Hussain et al., 2023). Therefore, tourism providers should pay close attention to tourist feedback and deliver exceptional experiences to foster positive WOM, ultimately boosting overall tourist satisfaction.

BI has a noteworthy positive effect on tourist satisfaction, confirming the findings of Ho et al. (2024). BI not only enhances tourists’ sense of trust and belonging but also increases satisfaction by shaping positive brand perceptions. A strong BI instills confidence in tourists that their choice of destination or service aligns with their expectations, thereby boosting overall satisfaction (Agapito & Sigala, 2024; Kumar et al., 2024; Tahir et al., 2024). Moreover, when tourism businesses face rapid changes in the market environment, BI, as a key resource for differentiated competition, can help tourism practitioners respond more effectively to shifts in tourist behavior (Ezzatian et al., 2025). By leveraging BI to strengthen competitiveness, businesses can better adapt to evolving tourism behaviors (Absah et al., 2024).

Tourism experience has a noteworthy positive effect on tourist satisfaction, in agreement with the findings of Su et al. (2023), highlighting the influence of tourism experience on satisfaction. When tourists receive high-quality service and enjoy rich experiences during their journey, their satisfaction significantly increases (Kim & So, 2022). Furthermore, the enhancement of tourist satisfaction largely depends on a comprehensive tourism experience, suggesting that attention to detail in service delivery and activity design can significantly affect overall satisfaction (Liu et al., 2023; Sthapit et al., 2025).

Tourism experience serves as a mediator in the link between online reviews and tourist satisfaction, in accordance with the results of Kang et al. (2022). Kang et al. (2022) analyzed numerous online reviews, revealing their effect on tourists’ experiences. Positive online reviews help tourists select suitable destinations, leading to a better experience during the trip (X. Sun et al., 2024). Conversely, negative or misleading reviews may create unrealistic expectations, resulting in disappointment during the trip and reducing overall satisfaction. Online reviews influence tourists’ experiences, enhancing their trust in the destination and sense of expectation fulfillment, which in turn increases their satisfaction (Guerreiro et al., 2025; Sun et al., 2023).

Tourism experience serves as a mediator in the relationship between WOM and tourist satisfaction. This study corroborates the findings of Schoner-Schatz et al. (2021), indicating that tourists evaluate a destination’s reputation based on multiple dimensions, such as scenic attractions, local cuisine, historical culture, and natural environment, which significantly influence their tourism experience. Positive WOM helps tourists select destinations that better suit their needs, leading to a higher-quality experience during their journey (Pham et al., 2025; Ribeiro & Kalro, 2023; Su et al., 2023). Conversely, if WOM information is inaccurate or biased, it may result in a poor experience, thereby reducing satisfaction (Ribeiro & Kalro, 2023). WOM shapes tourists’ expectations, enhancing their experience and increasing satisfaction with the destination.

Tourism experience acts as a mediator in the relationship between BI and tourist satisfaction, affirming the results of Sukaatmadja et al. (2023). This research explored the influence of BI on tourist satisfaction and assessed the mediating role of brand experience in this relationship. BI not only directly enhances tourists’ loyalty to the brand but also further increases loyalty and satisfaction through a positive brand experience (Ho et al., 2024; Mohamed & Ünsalan, 2025; Sukaatmadja et al., 2023). BI has a significant positive impact on the tourism experience, with a strong BI helping tourists form higher expectations and leading to a more enjoyable experience during their journey. Additionally, when tourists perceive consistency between the BI and their actual experience, their overall satisfaction increases significantly (Kumail et al., 2022).

Building on the existing literature, this study makes several contributions. First, it integrates online reviews, WOM, and BI into a unified research model, and, drawing on ECT (Oliver, 1980), systematically reveals the psychological mechanisms linking digital information perception to tourist satisfaction. The combination of multiple information sources within a single theoretical framework is relatively rare in tourism research and helps address the limitations of prior studies in which variables were examined in isolation and explanatory mechanisms were insufficiently developed. Second, this study not only investigates the direct relationships between the independent and dependent variables but also introduces tourism experience as a mediating variable, providing an in-depth analysis of how external information indirectly affects satisfaction through internal experiential processes. This approach uncovers the complete pathway linking information, experience, and satisfaction, extends the application of the experience economy theory, and offers a theoretical basis for future studies on chain or parallel mediation involving related variables. Finally, this study conducts an empirical analysis based on a sample of visitors to major tourist attractions in China, presenting the behavioral characteristics and decision-making logic of tourists in a non-Western cultural context under digitalization. This provides a culturally diverse perspective to enrich tourism behavior research dominated by Western contexts and offers contextually relevant empirical insights for destination management and digital marketing strategy development.

Implications

Theoretical Implication

First, this study integrates multiple information sources (online reviews, WOM, and BI) and tourism experience into a single theoretical model, using Expectation–Confirmation Theory as the core framework to systematically reveal how digital information perception influences satisfaction through tourists’ subjective experiences. Compared with prior studies that have largely focused on a single information source or a single pathway, this study constructs a comprehensive theoretical framework encompassing the interactive relationships among external information, internal experience, and satisfaction, thereby addressing the gap in existing literature regarding multi-factor integrative research.

Secondly, this research investigates the mediating role of tourism experience in the effect of online reviews, WOM, and BI on tourist satisfaction, highlighting the critical role of tourism experience in the interaction of multiple factors. By introducing the mediating effect of tourism experience, this research enriches the understanding of the mechanisms influencing tourist satisfaction, clearly demonstrating the indirect effects of online reviews, WOM, and BI on satisfaction. This invention offers a theoretical basis for future research that aims to investigate the mediating function of experience, in addition to expanding the scope of satisfaction studies.

Finally, the model in this study accounts for 71.9% of the variance in tourist satisfaction, highlighting its significant predictive capability and further confirming the model’s rationality and scientific rigor. This finding emphasizes the importance of combining several aspects into a single framework in addition to confirming the efficacy of a multidimensional model. The model provides a new theoretical perspective for satisfaction research, helping both academia and industry better understand the complex factors influencing tourist satisfaction and their underlying mechanisms.

Practical Implication

First, this study identifies tourism experience as the most critical factor influencing tourist satisfaction. Therefore, destination managers should prioritize enhancing on-site experiences. Specific measures include optimizing service processes, strengthening the training of front-line staff, and improving intelligent navigation systems and infrastructure to enhance visitor convenience and immersion. In addition, managers should ensure consistency between visitors’ expectations and their actual experiences to avoid discrepancies between promotional content and reality. Since online reviews and BI also have significant impacts on satisfaction, managers are advised to actively respond to feedback, encourage positive user engagement, and utilize user-generated content to promote the destination brand. These efforts can help build a clear, trustworthy, and attractive image of the destination.

Second, as direct participants in the tourism experience, tourists themselves should improve their awareness of information gathering and decision-making. The findings suggest that online reviews, WOM, and BI not only shape tourists’ expectations but also enhance satisfaction through their influence on the actual tourism experience. Tourists are encouraged to consult others’ reviews and assess brand reputation when planning their trips, making choices that align with their personal needs. Maintaining an open and positive mindset during the trip can foster engagement and emotional immersion, enhancing the overall experience. After the trip, tourists should also actively share their feedback and suggestions. This not only contributes to service improvement but also provides valuable reference information for other travelers, creating a virtuous cycle of information sharing.

Finally, local governments play a vital role in improving tourist satisfaction and shaping the destination image. Public investment in tourism-related infrastructure and services should be strengthened, particularly in areas such as transportation, sanitation, and safety, to ensure a high-quality visitor experience. At the same time, stronger regulation of tourism businesses and online platforms is needed to ensure the accuracy and transparency of information, fostering a trustworthy and equitable tourism environment. Moreover, governments should support regional brand development and the integration of culture and tourism. Encouraging community involvement, cultural heritage projects, and eco-tourism initiatives can help create distinctive, culturally rich experiences, thereby enhancing tourist satisfaction and boosting the destination’s overall competitiveness.

Limitations and Future Research

This study has some limitations. First, although this study sample includes several popular tourist attractions in Henan Province, it has certain regional limitations. The findings may not fully reflect other regions or international tourist destinations. Thus, future research could broaden the sample scope to encompass tourists from diverse cultural backgrounds and various types of destinations. Second, this study used self-reported questionnaires to measure variables such as online reviews, WOM, and tourism experience, which may be influenced by participants’ subjective biases, potentially introducing response bias. Future research could incorporate other methods, like in-depth interviews, observational studies, or third-party data, to improve objectivity and reliability. Third, this study employs a cross-sectional design, which, although revealing the mediating role of tourism experience in the relationships among online reviews, WOM, BI, and tourist satisfaction, still has certain limitations in causal inference. Future research could adopt longitudinal tracking or scenario-based experimental approaches to further examine the stability and dynamic changes of this mechanism across different time periods or tourism contexts, thereby strengthening the causal reasoning underpinning the conclusions.

Conclusion

This study, grounded in ECT, examines the relationships among online reviews, WOM, BI, tourism experience, and tourist satisfaction, with particular emphasis on the mediating role of tourism experience. An empirical study with 517 participants revealed that online reviews, WOM, and BI all have a notable positive effect on tourist satisfaction. Tourism experience acts as a mediator in the influence of online reviews, WOM, and BI on tourist satisfaction. Online reviews and WOM enhance satisfaction by shaping tourists’ expectations and choice decisions, while BI boosts satisfaction through strengthening tourists’ emotional connections. Overall, tourism experience spans the entire travel process and plays a crucial role in enhancing tourist satisfaction. These findings provide empirical evidence for tourism operators to optimize tourism services, enhance tourist satisfaction, and improve market competitiveness. Future study could investigate the relevance of these relationships across diverse cultural and economic contexts, providing broader theoretical support.

Footnotes

ORCID iD

Lixia Bo

Ethical Considerations

The researchers confirms that all research was performed in accordance with relevant guidelines/regulations applicable when human participants are involved (e.g., Declaration of Helsinki or similar). This study was approved by the Ethics Committee of Henan Finance University (Approval Number: 2024-0025).

Consent to Participate

The participants received oral and written information and provided written informed consent before participating in the study.

Author Contributions

Conceptualization, Methodology, Formal analysis and investigation, Writing – original draft preparation, Writing – review and editing, Supervision: Lixia Bo. The author has read and agreed to the published version of the manuscript.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Science and Technology Research Project of Henan Province, China (242102321145) Research on the application of artificial intelligence technology to enhance tourist well-being under the background of smart tourism. Supported by the Key Research Project of Higher Education Institutions in Henan Province (22A630007); Systematic Quantification and Path Selection for National Innovation High-lands (242400411139).

Declaration of Conflicting Interests

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request. The raw data are also accessible through Zenodo: .

References

Aboalganam

K. M.

AlFraihat

S. F.

Tarabieh

(2025). The impact of user-generated content on tourist visit intentions: The mediating role of destination imagery. Administrative Sciences, 15(4), Article 117. https://doi.org/10.3390/admsci15040117

Absah

Yuliaty

Anuar

N. A. M.

(2024). The role of brand image and facilities and services in halal tourism and the accessibility of Islamic cultural needs to enhance tourist satisfaction and Halal Tourism Economic Growth in Malaysia and Indonesia. Cuadernos de Economía, 47(133), 211–219. https://doi.org/10.32826/cude.v47i133.1321

Agapito

Sigala

(2024). Experience management in hospitality and tourism: Reflections and implications for future research. International Journal of Contemporary Hospitality Management, 36(13), 57–76. https://doi.org/10.1108/IJCHM-11-2023-1722

Anderson

E. W.

(1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17. https://doi.org/10.1177/109467059800100102

Aravindan

K. L.

Ramayah

Thavanethen

Raman

Ilhavenil

Annamalah

Choong

Y. V.

(2023). Modeling positive electronic word of mouth and purchase intention using theory of consumption value. Sustainability, 15(4), Article 3009. https://doi.org/10.3390/su15043009

Arranz Val

Puche Regaliza

J. C.

Antón Maraña

Aparicio Castillo

(2024). Factores determinantes para la satisfacción del turista en destinos de interior: Impacto sobre el problema de la despoblación. Investigaciones Turísticas, (27), 77–104. https://doi.org/10.14198/INTURI.24396

Arumugam

Nakkeeran

Subramaniam

(2023). Exploring the factors influencing heritage tourism development: A model development. Sustainability, 15(15), Article 11986. https://doi.org/10.3390/su151511986

(2025). Raw data_factors influencing tourist satisfaction in Henan Province [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15054680

Brown

J. D.

(2002). The Cronbach alpha reliability estimate. JALT Testing & Evaluation SIG Newsletter, 6(1).

10.

Cao

Liu

Wang

(2025). Influencing factors of tourist loyalty in China camping destinations based on Expectation Confirmation Theory: The mediating role of satisfaction and well-being. Humanities and Social Sciences Communications, 12(1), Article 1418. https://doi.org/10.1057/s41599-025-05803-x

11.

Chen

Kim

(2025). The role of social media in shaping brand equity for historical tourism destinations. Sustainability, 17(10), Article 4407. https://doi.org/10.3390/su17104407

12.

Chiwaridzo

O. T.

Chiwaridzo

(2024). The destination brand equity constructs’ unseen influence: How they mold tourists’ behavior and shape sustainable tourism in Zimbabwe. International Journal of Hospitality & Tourism Administration, 1–37. https://doi.org/10.1080/15256480.2024.2443461

13.

Contu

Dessí

Massidda

Ortu

(2024). Online reviews explain differences in coastal and inland tourists’ satisfaction. Scientific Reports, 14(1), Article 23607. https://doi.org/10.1038/s41598-024-74918-z

14.

De Pelsmacker

Dens

Kolomiiets

(2018). The impact of text valence, star rating and rated usefulness in online reviews. International Journal of Advertising, 37(3), 340–359. https://doi.org/10.1080/02650487.2018.1424792

15.

Ezzatian

Toghraee

M. T.

Torabi Farsani

(2025). Branding tourism destinations in the metaverse: Insights from professionals and researchers. Journal of Tourism Futures, 25. https://doi.org/10.1108/jtf-08-2024-0176

16.

Farias

A. R.

Simão

Reis

(2022). Expressions of gratitude applied to business: A lesson for managing online reviews. Human Behavior and Emerging Technologies, 2022(1), Article 5353185. https://doi.org/10.1155/2022/5353185

17.

Gao

Meng

Guo

Lin

(2024). Achieving destination sustainability: How tourist-to-tourist interaction quality affects tourist loyalty? PLoS One, 19(8), Article e0296944. https://doi.org/10.1371/journal.pone.0296944

18.

Gao

Wang

Dai

(2025). Understanding tourist destination loyalty in mega-sporting events: An extended expectation confirmation model study of the Chengdu Universiade. Journal of Quality Assurance in Hospitality & Tourism, 1–29. https://doi.org/10.1080/1528008X.2025.2457946

19.

George

O. A.

Ramos

C. M. Q.

(2024). Sentiment analysis applied to tourism: Exploring tourist-generated content in the case of a wellness tourism destination. International Journal of Spa and Wellness, 7(2), 139–161. https://doi.org/10.1080/24721735.2024.2352979

20.

Guerreiro

Pinto

Bagheri

deMatos

(2025). Broadening tourism experience and destination image: A cross-cultural approach between international and domestic tourists. European Journal of Tourism Research, 39, Article 3909. https://doi.org/10.54055/ejtr.v39i.3743

21.

Hair

J. F.

Jr. Hult

G. T. M.

Ringle

C. M.

Sarstedt

(2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

22.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

23.

P.-T.

M.-T.

Huang

M.-L.

(2024). Understanding the impact of tourist behavior change on travel agencies in developing countries: Strategies for enhancing the tourist experience. Acta Psychologica, 249, Article 104463. https://doi.org/10.1016/j.actpsy.2024.104463

24.

Hoang

Nguyen

Mai

Nghia

(2024). Factors affecting sustainable ecotourism in Vietnam: Mediating role of tourist satisfaction and attractive destination. Environmental Economics, 15, 149–163. https://doi.org/10.21511/ee.15(2).2024.11

25.

Hung

V. V.

Dey

S. K.

Vaculcikova

Anh

L. T. H.

(2021). The influence of tourists’ experience on destination loyalty: A case study of Hue City, Vietnam. Sustainability, 13(16), Article 8889. https://doi.org/10.3390/su13168889

26.

Hussain

Kanwel

Asif

Jameel

Hwang

(2023). Impact of tourism satisfaction and service quality on destination loyalty: A structural equation modeling approach concerning China Resort Hotels. Sustainability, 15(9), Article 7713. https://doi.org/10.3390/su15097713

27.

Islam

M. T.

Herjanto

Kumar

Amin

(2025). Online travel reviews and tourist destination choices: An extension of the information adoption model. Tourism Review International, 29(1), 17–32. https://doi.org/10.3727/194344225X17315216888907

28.

Jagani

Saboori-Deilami

(2025). Sustainability orientation, sustainability implementation, and brand image in service firms. Business Strategy and the Environment, 34(5), 5687–5698. https://doi.org/10.1002/bse.4273

29.

Jebbouri

Zhang

Wang

Bouchiba

(2021). Exploring the relationship of image formation on tourist satisfaction and loyalty: Evidence from China. Frontiers in Psychology, 12, Article 748534. https://doi.org/10.3389/fpsyg.2021.748534

30.

Jimenez-García

Espinoza Heredia

Cruz Lizana

Cruz-Tarrillo

J. J.

Millones-Liza

D. Y.

(2025). Destination image and brand value as predictors of tourist behavior: Happiness as a mediating link. Administrative Sciences, 15(5), Article 176. https://doi.org/10.3390/admsci15050176

31.

Kang

Sun

Liang

Mao

H.-Y.

(2022). A study on the influence of online reviews of new products on consumers’ purchase decisions: An empirical study on JD.com. Frontiers in Psychology, 13, Article 983060. https://doi.org/10.3389/fpsyg.2022.983060

32.

Kim

K. K. F.

(2022). Two decades of customer experience research in hospitality and tourism: A bibliometric analysis and thematic content analysis. International Journal of Hospitality Management, 100, Article 103082. https://doi.org/10.1016/j.ijhm.2021.103082

33.

Kline

R. B.

(2018). Response to Leslie Hayduk’s review of principles and practice of structural equation modeling, 4th Edition. Canadian Studies in Population, 45(3–4), Article 188. https://doi.org/10.25336/csp29418

34.

Kotler

(2004). A framework for marketing management. Pearson Education India.

35.

Kumail

Qeed

M. A. A.

Aburumman

Abbas

S. M.

Sadiq

(2022). How destination brand equity and destination brand authenticity influence destination visit intention: Evidence from the United Arab Emirates. Journal of Promotion Management, 28(3), 332–358. https://doi.org/10.1080/10496491.2021.1989540

36.

Kumar

Rajaguru

Yang

(2024). Investigating how brand image and attitude mediate consumer susceptibility to eWOM and purchase intention: Comparing enterprise-owned vs. third-party online review websites using multigroup analysis. Journal of Retailing and Consumer Services, 81, Article 104051. https://doi.org/10.1016/j.jretconser.2024.104051

37.

Larsen

(2007). Aspects of a psychology of the tourist experience. Scandinavian Journal of Hospitality and Tourism, 7, 7–18. https://doi.org/10.1080/15022250701226014

38.

Limonta

Ruiz-Moreno

Ali

A. A.

Zingone

(2024). The Manta mystique: Destination image and its effect on tourist citizenship behavior and willingness to sacrifice. Journal of Vacation Marketing, 20. https://doi.org/10.1177/13567667241274548

39.

Lin

Ryu

Ahn

Y.-J.

(2024). Effect of product design on repurchase intention, electronic word-of-mouth, and museum visit intention: Museum random boxes in China. International Journal of Tourism Research, 26(3), Article e2657. https://doi.org/10.1002/jtr.2657

40.

Liu

Zhu

Fang

(2025). Self-congruity and functional congruity drive positive word-of-mouth in food tourism through moderating effects of emotional experiences. Scientific Reports, 15(1), Article 10560. https://doi.org/10.1038/s41598-025-94046-6

41.

Liu

Feng

(2022). Process vs. outcome: Effects of food photo types in online restaurant reviews on consumers’ purchase intention. International Journal of Hospitality Management, 102, Article 103179. https://doi.org/10.1016/j.ijhm.2022.103179

42.

Liu

Zhou

Sun

(2023). The impact of the wellness tourism experience on tourist well-being: The mediating role of tourist satisfaction. Sustainability, 15(3), Article 1872. https://doi.org/10.3390/su15031872

43.

Liu

Meng

(2024). How online reviews of sports tourism affect tourists’ value co-creation behavior: Findings from PLS-SEM, NCA, and fsQCA. Journal of Hospitality and Tourism Management, 60, 360–372. https://doi.org/10.1016/j.jhtm.2024.07.005

44.

Meade

Craig

(2012). Identifying careless responses in survey data. Psychological Methods, 17, 437–455. https://doi.org/10.1037/a0028085

45.

Miah

Haq

M. M.

Biswas

Szabo-Szentgroti

Walter

(2025). Effects of tourists’ perceived crowding on tourist loyalty based on overcrowded tourist attraction in Bangladesh: The mediating role of destination attractiveness. International Journal of Tourism Research, 27(2), Article e2764. https://doi.org/10.1002/jtr.2764

46.

Mohamed

Z. A.

Ünsalan

(2025). Does AI marketing efforts affect brand loyalty of Gen Z consumers? Mediating role of brand image and brand experience. Cogent Business & Management, 12(1), Article 2542428. https://doi.org/10.1080/23311975.2025.2542428

47.

Nawawi

Ilmawan

K. F.

Maarif

M. R.

Syafrudin

(2024). Exploring tourist experience through online reviews using aspect-based sentiment analysis with zero-shot learning for hospitality service enhancement. Information, 15(8), Article 499. https://doi.org/10.3390/info15080499

48.

Nguyen

H. T.

A. T. T.

Phan

A. C.

Hoang

T. D. L.

(2022). A multi-perspective approach of international tourist satisfaction in tourism service: From big data perspective. Journal of Asia Business Studies, 17(4), 850–872. https://doi.org/10.1108/jabs-03-2022-0090

49.

Nian

Bao

Chen

Zhang

Chen

(2025). How does the tourist experience affect the conservation of World Heritage Sites via the stimulus–organism–response model? Mount Sanqingshan National Park, China. NPJ Heritage Science, 13(1), Article 21. https://doi.org/10.1038/s40494-025-01550-z

50.

Nian

Chen

Yan

(2024). Tourist crowding versus service quality: Impacting mechanism of tourist satisfaction in world natural heritage sites from the mountain Sanqingshan National Park, China. Sustainability, 16(18), Article 8268. https://doi.org/10.3390/su16188268

51.

Nie

Q. Q.

Zeng

(2024). Impact of brand image on the brand experience of consumers in China: Brand loyalty as a mediator. Social Behavior and Personality, 52(7), Article e13179. https://doi.org/10.2224/sbp.13179

52.

Oliver

R. L.

(1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405

53.

Oliver

R. L.

(2010). Satisfaction: A behavioral perspective on the consumer. M. E. Sharpe Incorporated.

54.

Pham

T. V.

Duc Le

Dang Thi

K. T.

Nguyen

T. L.

Tran

T. N. T.

(2025). Unveiling the impacts of eWOM on tourist revisit intention from a cognitive perspective: The moderating role of trade-offs. Cogent Business & Management, 12(1), Article 2452239. https://doi.org/10.1080/23311975.2025.2452239

55.

Pooja

Upadhyaya

(2025). Does negative online review matter? An investigation of travel consumers. International Journal of Consumer Studies, 49(2), Article e70043. https://doi.org/10.1111/ijcs.70043

56.

Pratminingsih

S. A.

Munawar

Rudatin

C. L.

(2025). Creative tourism experience: Exploring the influence of novelty-seeking, perceived coolness and servicescape on behavioral intention. Cogent Business & Management, 12(1), Article 2447906. https://doi.org/10.1080/23311975.2024.2447906

57.

Preacher

K. J.

Hayes

A. F.

(2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

58.

Ribeiro

D. A.

Kalro

A. D.

(2023). Four decades of negative word-of-mouth and negative electronic word-of-mouth: A morphological analysis. International Journal of Consumer Studies, 47(6), 2528–2552. https://doi.org/10.1111/ijcs.12962

59.

Schoner-Schatz

Hofmann

Stokburger-Sauer

N. E.

(2021). Destination’s social media communication and emotions: An investigation of visit intentions, word-of-mouth and travelers’ facially expressed emotions. Journal of Destination Marketing & Management, 22, Article 100661. https://doi.org/10.1016/j.jdmm.2021.100661

60.

Seow

A. N.

Foroughi

Choong

Y. O.

(2024). Tourists’ satisfaction, experience, and revisit intention for wellness tourism: E word-of-mouth as the mediator. Sage Open, 14(3), Article 21582440241274049. https://doi.org/10.1177/21582440241274049

61.

Shi

Chen

Liu

(2025). Evaluating cruise user satisfaction through online reviews: A method based on sentiment analysis and large-scale group decision-making. Applied Intelligence, 55(6), Article 418. https://doi.org/10.1007/s10489-025-06241-7

62.

Shmueli

Sarstedt

Hair

J. F.

Cheah

J.-H.

Ting

Vaithilingam

Ringle

C. M.

(2019). Predictive model assessment in PLS-SEM: Guidelines for using PLS predict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

63.

Stanovčić

Manojlović

Perovic

(2021). The relationship between cultural tourist experience and recommendation intention: Empirical evidence from Montenegr. Sustainability, 13(23), Article 13144. https://doi.org/10.3390/su132313144

64.

Sthapit

Garrod

Ibrahim

Björk

(2025). Memorable wellness tourism experiences: Antecedents that lead to enjoyable outcomes. International Journal of Spa and Wellness, 8(2), 155–181. https://doi.org/10.1080/24721735.2025.2473124

65.

Pan

Wen

Phau

(2023). Effects of tourism experiences on tourists’ subjective well-being through recollection and storytelling. Journal of Vacation Marketing, 29(4), 479–497. https://doi.org/10.1177/13567667221101414

66.

(2025). Awakening the soul during travel: Influence mechanism of memorable tourism experience on university students’ life meaning. Frontiers in Psychology, 16, Article 1521716. https://doi.org/10.3389/fpsyg.2025.1521716

67.

Sukaatmadja

I. P. G.

Yasa

N. I. N. K.

Rahmayanti

P. L. D.

(2023). Bali brand love: A perspective from domestic tourists. Cogent Business & Management, 10(3), Article 2260119. https://doi.org/10.1080/23311975.2023.2260119

68.

Sun

Kang

Zhao

(2023). How online reviews with different influencing factors affect the diffusion of new products. International Journal of Consumer Studies, 47(4), 1377–1396. https://doi.org/10.1111/ijcs.12915

69.

Sun

Qiu

Feng

(2024). Impact of electronic word-of-mouth dispersion on tourists’ purchase intentions for local food restaurants. Journal of Hospitality & Tourism Research, 50(2), 256–270. https://doi.org/10.1177/10963480241311116

70.

Sun

Wang

Zhou

Wang

(2024). Segmenting tourists’ motivations via online reviews: An exploration of the service strategies for enhancing tourist satisfaction. Heliyon, 10(1), Article e23539. https://doi.org/10.1016/j.heliyon.2023.e23539

71.

Tahir

A. H.

Adnan

Saeed

(2024). The impact of brand image on customer satisfaction and brand loyalty: A systematic literature review. Heliyon, 10(16), Article e36254. https://doi.org/10.1016/j.heliyon.2024.e36254

72.

Tešin

Kovačić

Bratić

Obradović Strålman

(2025). Toward destination competitiveness: Exploring the power of attributes in shaping memorable experiences and its effect on tourist satisfaction and loyalty. International Journal of Hospitality & Tourism Administration, 1–30. https://doi.org/10.1080/15256480.2025.2521783

73.

Vidosavljević

Đurić

(2025). EWOM as a key factor in consumer intent to visit a destination: The moderating role of trust. Hotel and Tourism Management, 13, Article 23. https://doi.org/10.5937/menhottur2500006V

74.

World Travel & Tourism Council. (2025). China surges ahead: Travel & tourism sector forecast to hit a record ¥13.7TN this year. https://wttc.org/news/china-surges-ahead-travel-and-tourism-sector-forecast-to-hit-a-record-13-7tn-this-year

75.

M.-H.

(2025). The impact of brand identification, brand image, and brand love on brand loyalty: The mediating role of customer value co-creation in hotel customer experience. Frontiers in Communication, 10, Article 1626744. https://doi.org/10.3389/fcomm.2025.1626744

76.

Cheung

L. T. O.

Lovett

Duan

Pei

Liang

(2023). Understanding the influence of user-generated content on tourist loyalty behavior in a cultural World Heritage Site. Tourism Recreation Research, 48(2), 173–187. https://doi.org/10.1080/02508281.2021.1913022

77.

Yadav

Verma

Chikhalkar

(2023). Online reviews towards reducing risk. Journal of Tourism Futures, 10(2), 299–316. https://doi.org/10.1108/JTF-01-2022-0016

78.

Wei

Wen

Ying

Tan

(2021). Creating memorable experience in rural tourism: A comparison between domestic and outbound tourists. Journal of Travel Research, 60(7), 1527–1542. https://doi.org/10.1177/0047287520951641

79.

Yuan

Marzuki

(2024). What keeps historical theme park visitors coming? Research based on expectation confirmation theory. Frontiers in Psychology, 15, Article 1293638. https://doi.org/10.3389/fpsyg.2024.1293638

80.

Zhang

Gao

(2022). Research on the evolution of consumers’ purchase intention based on online reviews and opinion dynamics. Sustainability, 14(24), Article 16510. https://doi.org/10.3390/su142416510

81.

Zhou

Zhang

Jiang

(2025). Research on the influence path of red tourism experience on tourists’ well-being: Based on the chain mediating role of tourists’ emotion and national identity. BMC Psychology, 13(1), Article 105. https://doi.org/10.1186/s40359-025-02435-3

82.

Zhou

Wang

(2024). An analysis of the relationship linking immersive tourism experiencescape and emotional experience to tourists’ behavioral intentions. Sustainability, 16(17), Article 7598.

How Online Reviews,Word-of-Mouth,Brand Image,and Tourism Experience Influence Tourist Satisfaction?

Abstract

Keywords

Introduction

Literature Review and Hypotheses

Expectation-Confirmation Theory

Online Reviews and Tourist Satisfaction

Word-of-Mouth and Tourist Satisfaction

Brand Image and Tourist Satisfaction

Tourism Experience and Tourist Satisfaction

The Mediating Role of Tourism Experience

Methods

Research Design

Data Collection

Measurement Tools

Online Reviews Scale

Word-of-Mouth Scale

Brand Image Scale

Tourism Experience Scale

Tourist Satisfaction Scale

Statistical Analysis

Results

Descriptive Analysis

Measurement Model

Structural Model

Collinearity Test

Path Hypothesis Test

Model Interpretability

Analysis of Mediating Effect

Discussion

Implications

Theoretical Implication

Practical Implication

Limitations and Future Research

Conclusion

Footnotes

ORCID iD

Ethical Considerations

Consent to Participate

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References