Destination image and its effect on tourist behavioral intentions: systematic review

Imagem do destino e seu efeito nas intenções comportamentais dos turistas: revisão sistemática

Imagen del destino y su efecto en las intenciones de comportamiento del turista: una revisión sistemática

Abstract

Destination image constitutes a fundamental predictor of tourism behavior. The present study aims to analyze the effect of destination image on tourists’ behavioral intentions through a systematic review that explores the elements that make up this relationship in the three stages of travel. Based on the analysis of 78 studies published between 2010 and 2024 in high-impact academic journals, this paper provides a holistic view of how destination image’s cognitive, affective, and conative dimensions shape tourists’ decisions and behaviors before, during, and after the trip. The results show that destination image is key in shaping the intention to visit in the pre-trip stage, place attachment during the trip, and post-visit satisfaction and loyalty. In addition, research gaps were identified, such as the need to explore the impact of new technologies on destination image. Practical implications suggest that effective DI management can improve tourist satisfaction and loyalty.

Keywords
Destination image; Cognitive image; Affective image; Overall image; Behavioral intentions

Resumo

A imagem do destino constitui um preditor fundamental do comportamento turístico. O presente estudo visa analisar o efeito da imagem do destino nas intenções comportamentais dos turistas por meio de uma revisão sistemática que explora os elementos que compõem essa relação nas três etapas da viagem. Com base na análise de 78 estudos publicados entre 2010 e 2024 em periódicos acadêmicos de alto impacto, este artigo oferece uma visão holística de como as dimensões cognitiva, afetiva e conativa da imagem do destino moldam as decisões e os comportamentos dos turistas antes, durante e depois da viagem. Os resultados mostram que a imagem do destino é fundamental para moldar a intenção de visitar o local na fase pré-viagem, o apego ao lugar durante a viagem e a satisfação e fidelização pós-viagem. Além disso, foram identificadas lacunas na pesquisa, como a necessidade de explorar o impacto das novas tecnologias na imagem do destino. As implicações práticas sugerem que uma gestão eficaz da imagem do destino pode melhorar a satisfação e a fidelização dos turistas.

Palavras-chave
Imagem do destino; Imagem cognitiva; Imagem afetiva; Imagem geral; Intenções comportamentais

Resumen

La imagen del destino constituye un predictor fundamental del comportamiento turístico. El presente estudio tiene como objetivo analizar el efecto de la imagen del destino en las intenciones de comportamiento de los turistas a través de una revisión sistemática que explora los elementos que conforman esta relación en las tres etapas del viaje. A partir del análisis de 78 estudios publicados entre 2010 y 2024 en revistas académicas de alto impacto, este artículo ofrece una visión holística de cómo las dimensiones cognitiva, afectiva y conativa de la imagen del destino moldean las decisiones y comportamientos de los turistas antes, durante y después del viaje. Los resultados muestran que la imagen del destino es clave para conformar la intención de visita en la etapa previa al viaje, el apego al lugar durante la estancia y la satisfacción y lealtad posteriores a la visita. Además, se identificaron brechas de investigación, como la necesidad de explorar el impacto de las nuevas tecnologías en la imagen del destino. Las implicaciones prácticas sugieren que una gestión efectiva de la imagen del destino puede mejorar la satisfacción y fidelización de los turistas.

Palabras clave
Imagen del destino; Imagen cognitiva; Imagen afectiva; Imagen global; Intenciones de comportamiento

1 INTRODUCTION

Tourism has grown significantly in recent decades due to globalization and demand for unique experiences. Studying travelers’ behavioral intentions (BI) is decisive for destination development (Chen & Tsai, 2007; Karim et al., 2023; Qu et al., 2011). A fundamental antecedent of tourism behavior is destination image (DI) (Stylidis et al., 2015), recognized as a powerful management tool to drive the tourism industry in the global market (Afshardoost & Eshaghi, 2020) and receiving significant attention for its predictive role in destination selection and travel behavior (Baloglu, 2000).

The DI and BI relationship can be classified into three stages: pre-trip, during, and post-trip (Ren et al., 2021). Tourists’ pre-trip behavior refers to the intention and decision to travel; during the trip, it includes place attachment-brand attachment; and post-trip encapsulates satisfaction, loyalty, revisit intention, recommendation intention, and word-of-mouth (Chu et al., 2022).

Given the central role of DI in tourism research, numerous literature reviews have examined different aspects of this construct. Early reviews focused on the conceptualization and measurement of DI (Echtner & Ritchie, 2003; Gallarza et al., 2002; Pike, 2002), while later studies analyzed trends in DI research and identified emerging themes and methodological developments (Stepchenkova & Mills, 2010; Huang et al., 2021; Primananda et al., 2022). More recent studies have also explored specific dimensions of DI and its relationship with tourism outcomes (Chu et al., 2022; Wang et al., 2023).

Despite this growing body of literature, research on DI and tourists’ BI remains fragmented. Many studies examine specific relationships—such as DI and satisfaction, loyalty, or revisit intention—without integrating these findings within the broader tourist journey. Moreover, most existing reviews either focus on particular aspects of DI or analyze limited periods of the literature. Only a few studies have explicitly addressed the relationship between DI and BI, and these generally focus on specific behavioral outcomes or particular stages of the trip (Afshardoost & Eshaghi, 2020; Borlido & Kastenholz, 2021).

In addition, the digital transformation of tourism has significantly reshaped the processes through which DI are formed and disseminated. The growing influence of social media, user-generated content, virtual reality, and artificial intelligence in destination marketing and tourist decision-making has altered the way tourists perceive destinations and how DI translates into BI (Lam et al., 2020; Lin et al., 2021). These technological developments indicate that traditional conceptualizations of the DI–BI relationship may not fully capture the complexity of contemporary tourism dynamics.

Consequently, the growing number of studies examining DI and tourists’ BI has produced a fragmented body of knowledge dispersed across different contexts, variables, and stages of the tourist experience. Although numerous studies have explored specific relationships between DI and behavioral outcomes, no systematic review has comprehensively synthesized how the DI–BI relationship evolves across the different stages of the tourist journey. This fragmentation highlights the need for integrative research capable of consolidating these findings into a coherent framework that clarifies how DI influences BI throughout the entire travel cycle.

The present study aims to analyze the influence of DI on tourists’ BI across the different stages of the travel experience: pre-trip, during-trip, and post-trip. As well as identifying the current state of research and its gaps to propose future research. While previous reviews have examined specific dimensions of destination image or focused on particular behavioral outcomes, this study distinguishes itself by integrating the DI–BI relationship across the three stages of the tourist journey. By adopting a stage-based perspective, this review provides a more comprehensive and dynamic understanding of how destination image influences tourist behavior, moving beyond fragmented approaches that analyze isolated variables or limited timeframes. This integrative perspective contributes to a more coherent theoretical understanding of the DI–BI relationship as a process that evolves across the travel experience.

1.1 Destination image

Since the 1970s, DI has been extensively studied for its relevance in marketing and destination management (Cherifi et al., 2014; Fu et al., 2016; Hosany et al., 2006; Stepchenkova & Li, 2014; Sun et al., 2015; Tan, 2017). Most researchers consider the DI as a multidimensional construct (Añaña et al., 2016; Chu et al., 2022; Su et al., 2020), which is approached from different approaches: conceptualization, structure, formation, and measurement (Lai, 2018).

There is currently quite a consensus in the literature that DI is the sum of beliefs, impressions, and opinions that a person has about a certain place or destination (Baloglu & Brinberg, 1997; Crompton, 1979; Kotler et al., 1993; Lai et al., 2018; Sun et al., 2021; Zhang et al., 2014). Previous studies recognize three types of images: pre-visit image, during-visit image, and post-visit image (Gholamhosseinzadeh et al., 2021; Martín et al., 2017) because tourists’ image perception towards a destination varies according to these travel stages (Kim et al., 2019). Hence, DI is important because of its predictive role in destination selection and travel behavior (Baloglu, 2000). Also, recent studies highlight that elements such as culture, sustainability, and territorial identity significantly influence how destinations are perceived by tourists and how they are positioned within increasingly competitive tourism markets (Silva & Souza, 2018; Wang et al., 2023; Zhang et al., 2021).

Regarding the conceptual structure of DI, Echtner & Ritchie (1991) proposed the three continuous components classified into six dimensions: Functional-Psychological, Attributes-Holistic, and Common-Unique from previous revisions of definitions. On the other hand, Gartner (1994) proposes the three components that make up the DI: cognitive, affective, and conative. Bramwell and Rawding (1996) conceptualized a new approach to understanding DI by defining them in the dimensions of projected and perceived images.

A large body of previous research reveals the role of cognitive and affective images in tourists’ BI (Bigne et al., 2009; Chew & Jahari, 2014; Hosany et al., 2006). The cognitive image comprises knowledge and beliefs about a destination’s attributes, resources, and attractions (Govers et al., 2007; Papadimitriou et al., 2015), functional- tangible, and intangible components (Añaña et al., 2016; Jani & Nguni, 2016; Papadimitriou et al., 2018). Affective image refers to the feelings and emotions that a destination evokes in visitors (Hallmann et al., 2015; Prayag & Ryan, 2012; Stepchenkova & Li, 2012). The interactions of cognitive and affective perceptions give rise to the overall image through a comprehensive evaluation of the place (Baloglu & McCleary, 1999; Stern & Krakover, 1993). Or as posited by Josiassen et al. (2016) a DI construct, determined as an overall evaluative perception of a tourist towards a destination.

The conative dimension of image is the active consideration of tourists towards a place as a potential travel destination (Gartner, 1994). Authors such as Bagozzi (1992) define conative as a behavioral disposition, prior or antecedent to intention. However, the term conation has been used interchangeably with intention or desire (White, 2014). The tourism literature has not paid sufficient attention to the conative aspects of DI, and its importance in predicting behavioral attitudes and intentions (Tasci, 2009; White, 2014).

Although these conceptualizations have significantly contributed to understanding destination image, each framework presents certain limitations when capturing the complexity of tourists’ perceptions. For instance, models based on the cognitive–affective–conative structure provide a comprehensive representation of the psychological processes underlying destination evaluation. However, they may oversimplify the dynamic and context-dependent nature of destination image formation. Similarly, approaches that distinguish between projected and perceived images highlight the role of marketing communication and destination branding, yet they may not fully account for tourists’ experiential and emotional responses during the travel process. These debates illustrate that DI remains a multifaceted and evolving construct, which has important implications for tourism research, particularly when examining its influence on tourists’ BI across different stages of the travel experience.

1.2. Behavioral intentions

Despite the importance of BI as a predictor of tourist behavior, an important theoretical challenge remains the gap between intentions and actual behavior. While strong intentions generally increase the likelihood of performing a behavior, empirical studies have shown that intentions do not always translate into real actions. This intention–behavior gap has received increasing attention in tourism research, as various situational, psychological, and contextual factors may influence whether tourists ultimately act on their intentions. Recent studies have examined the mechanisms that reinforce the relationship between intentions and behavior and recognize the influence of factors such as experience, satisfaction, destination attachment, and contextual conditions throughout the travel process.

BI has become a fundamental variable to measure the success of a tourism destination, remaining a crucial area of research in the fields of marketing and tourism (Prayag et al., 2013). BI, as the immediate antecedent of actual behavior (Fishbein & Ajzen, 1975), has been one of the most studied topics in the field of tourism. This refers to the intended future behavior of an individual (Oliver & Swan, 1989) and is considered fundamental to predict actual behavior (Hsu et al., 2010).

According to Perugini & Bagozzi (2004), BI as partial planning involves some forms of commitment and are predicted by pre-intention variables, such as affective or conative aspects of the image (White, 2014). Likewise, strong intentions provide better predictions of behavior, thus reducing the gap between intention and behavior (Conner & Norman, 2022).

Moreover, these BI can be classified as favorable or unfavorable. Favorable intentions reflect conative loyalty (Chen and Chen, 2010) and encompass actions such as positive recommendation or word of mouth, paying a higher price, higher spending, and continued loyalty (Ardani et al., 2019). For example, a positive image of the destination increases the tendency of tourists to rate it positively, which in turn reinforces their predisposition to return and recommend it to others (Bigné et al., 2001; De Nisco et al., 2015).

BI is an essential variable in tourism, because it allows to examine the motives of travelers in the selection of destinations, as well as their behaviors in the stages of the trip (Baloglu, 2000; Afshardoost & Eshaghi, 2020). Previous studies on the impact of Di on tourist behaviors have been based primarily on pre- and post-trip behaviors (Borlido & Kastenholz, 2021). For researchers and tourism managers the most favorable variables for pre- and post-visit BI constitute intention to visit (Fu et al., 2016), intention to revisit (Loi et al., 2017) and intention to recommend (Prayag & Ryan, 2012; Molinillo et al., 2018).

Numerous academic studies have evidenced the effect of DI on BI. Pre-trip; as an attraction variable (Kim, 2018; Sancho & Alvarez, 2010; Zhang et al., 2014), trip intention (Lee & Jeong, 2018; Elahi et al., 2020), choice or decision (Añaña et al., 2016; Agapito et al., 2013; Bigne et al., 2001; Chon, 1991; Echtner & Ritchie, 1991; Farmaki, 2012; Gallarza et al., 2002; Heitmann, 2011; Konecnik & Gartner, 2007; Mayo, 1973; Mendes-Filho et al. , 2018; Tasci & Gartner, 2007), during the trip; perception/experience (Añaña et al., 2016; Chen & Tsai, 2007; Liu et al., 2015), attitude/preference (Deng & Li, 2014; Al-Kwifi, 2015), place attachment (Jiang et al., 2017; Liu et al. (2020), and post-trip satisfaction (Jeong & Kim, 2020; Kim, 2018; Nikolova & Hassan, 2013; Pearce, 1997; Prayag & Ryan, 2012, Veasna et al. 2013), satisfaction/loyalty (Albaity & Melhem, 2017; Kim, 2018; Tasci et al., 2022; Xue & Lee, 2020), return or revisit destination (Chew & Jahari, 2014; Loi et al., 2017; San Martín et al., 2018; Stylos et al., 2016, 2017; Woosnam et al., 2020); WOM/eWOM and BI (Agapito et al, 2013; Bigné et al., 2001; Konecnik & Gartner, 2007, Papadimitriou et al., 2015; Qu et al., 2011; Stylidis et al., 2017: Tan, 2017).

Over time, research has examined the relationship between DI and tourists’ BI through different theoretical perspectives, explaining how the cognitive, affective, and conative dimensions of destination image influence tourists’ behavioral responses throughout the travel experience. Understanding how these approaches have evolved and interacted in literature is essential for interpreting the diverse empirical findings in this field. Therefore, the present review builds on these conceptual foundations to organize and synthesize existing studies on the DI–BI relationship across the pre-trip, during-trip, and post-trip stages.

2 METHODOLOGY

The systematic review followed the SALSA framework (Grant & Booth, 2009), which structures the review process into four stages: search, appraisal, synthesis, and analysis. Within the appraisal stage, the PRISMA guidelines (Page et al., 2021) were used to ensure transparency in the identification, screening, and selection of studies. To ensure a systematic and transparent review process, this study is guided by the following research question: How does destination image (DI) influence tourists’ behavioral intentions (BI) across the different stages of the travel experience (pre-trip, during-trip, and post-trip)?

As shown in Table 1, during the Search phase, the databases Scopus and Web of Science were consulted using the keywords “destination image” and “behavioral intentions” to identify tourism-related articles published between 2010 and 2024. These terms were selected because, in this study, they are conceptualized as umbrella constructs encompassing a wide range of cognitive, affective, and conative dimensions, as well as multiple behavioral outcomes such as revisit intention, recommendation, and loyalty. This broad conceptualization allows for capturing the conceptual heterogeneity present in the literature and facilitates a systematic and integrative synthesis of the DI–BI relationship across different contexts and stages of the travel experience.

In the Evaluation phase, 560 articles were screened and duplicates were removed. Subsequently, 62 articles published in the top 10 tourism journals ranked by Scimago (SJR 2023) and indexed in SSCI were selected. Additionally, 12 highly cited articles were included to capture influential contributions in the DI–BI literature. These studies were identified through citation counts retrieved from Scopus and Web of Science and selected when they ranked among the most cited within the initial search results, regardless of journal ranking. In total, 78 articles were selected for in-depth analysis. The procedures described above are summarized in Table 1.

Table 1
SALSA methodology

Stage	Criteria
Search	Databases: Scopus, Web of Science Keywords: destination image, behavioral intentions Language: English Date: 2010-2024
Appraisal	Number (selected through PRISMA 2020 criteria): 78 articles selected from 560 papers. Inclusion criteria - Peer-reviewed journal articles. -From the ten highest impact journals according to Scimago Journal Ranking for the Hospitality Leisure Sport Tourism area and the Social Sciences Citation Index (SSCI). -Articles that explicitly examined the relationship between destination image and tourist behavioral intentions. Exclusion criteria - Studies not addressing the DI–BI relationship. -Studies focusing on unrelated constructs. -Studies lacking sufficient empirical or theoretical rigor. -Duplicate records. Countries: Not limited in geographic area
Synthesis	Approach, key concepts Protocol with the coding variables of the papers. Collection of reference data (journals, authors, quality of articles) and structured summaries (object of study, theory and hypotheses, methodology, variables and main results).
Analysis	Proposal Descriptive and thematic content analysis based on the coded variables to identify the main research themes and the relationships reported between DI dimensions and tourists’ BI across the pre-trip, during-trip, and post-trip stages.

Specifically, the search strategies followed for the two databases consulted are presented in Table 2:

Table 2
Search equations

Databases	Equations
Scopus	Period: 2010-2024 Type of sources: Journal articles Field: Title / Abstract / Keywords Equation: TITLE-ABS-KEY (destination AND image) AND TITLE-ABS-KEY (behavioral AND intentions)
Web of Science	Period:2010-2024 Type of sources: Journal articles Field: All fields Equation: TS = ("destination image") AND TS = ("behavioral intentions")

In the Synthesis stage, the papers selected were coded based on a protocol consisting of 18 variables grouped into four categories shown in Table 3 below:

Table 3
Variables selected for coding the papers

Characteristics of the journals	Publisher; country; year started; number of articles published; quartile of the journal; Scimago Journal Rank; indexation; total citations (last 3 years); citations per paper (last 2 years)
Scientific production	Annual scientific production; country scientific production
Authors and organizations	Authors with a higher number of publications; frequency of number of publications per author
Document contents	Most prevalent keywords; thematic mapping; variables used; path relationship between variables; results

In the Analysis phase, the selected studies were examined through content analysis to identify the main themes, variables, and relationships reported in the literature. Particular attention was given to the relationships between destination image (DI) and tourists’ behavioral intentions (BI), as well as to the antecedents, mediators, moderators, and outcomes associated with this relationship. The findings were then organized according to the three stages of the travel experience: pre-trip, during-trip, and post-trip.

To ensure the reliability of the coding process, the selected articles were independently coded by two researchers using the predefined coding variables. Any discrepancies were discussed and resolved through consensus. A bibliometric analysis was conducted using Zotero for reference management and Bibliometrix to generate frequency tables and thematic maps.

The search and evaluation procedures are illustrated in Figure 1 using the PRISMA 2020 flow diagram (Page et al., 2021), in total 78 articles were included.

Figure 1
Flowchart of the paper selection process based on the Preferred Reporting Elements for Systematic Reviews and Meta-Analyses

3 FINDINGS

Performance analysis involves quantitative assessment of data based on activity indicators (Mingers & Leydesdorff, 2015). This section analyzes the field’s performance with the evaluation of productivity (publications), impact (citations), and contributors (authors, sources, countries) by using different metrics to determine the most relevant authors, journals, institutions, and countries within the research area.

3.1. Characteristics of the journals

In Table 4, 50% of the selected journals are published by Elsevier Ltd., and 80% are headquartered in the United Kingdom. Among those with the most publications on this subject are Tourism Management (23), and with the same number, Journal of Travel Research and Journal of Destination Marketing and Management (16). These journals have the longest-lived journals are one with more than 50 years of experience, while three are more than 40 years old, one is more than 30 years old and the other is 30 years old. The youngest dates from 2012.

Table 4
Journals with the most publications on the subject, ranking, publisher, country, number of publications selected, and citations per document in the last 2 years

Journals	Publisher	CountryStart y	Articles	IF (quartile)		TCD 2-y
				SJR	SSCI
Tourism Management	Elsevier	UK 1982	23	3.352 (Q1)	2.81 (Q1)	14.57
Journal of Travel Research	SAGE Publications	UK 1969	16	3.408 (Q1)	2.07 (Q1)	13.38
Journal of Destination Marketing & Management	Elsevier	UK 2012	16	2.450 (Q1)	2.19 (Q1)	11.67
Journal of Hospitality and Tourism Management	Elsevier	UK 2006	7	2.050 (Q1)	1.98 (Q1)	9.12
International Journal of Contemporary Hospitality Management	Emerald Publishing.	UK 1989	5	2.843 (Q1)	2.4 (Q1)	11.63
Annals of Tourism Research	Elsevier	UK 1982	3	3.447 (Q1)	3.46 (Q1)	9.42
Journal of Hospitality Marketing & Management	Routledge	US 2009	3	3.447 (Q1)	2.51 (Q1)	15.39
International Journal of Hospitality Management	Elsevier	UK 1982	2	2.923 (Q1)	2.74 (Q1)	13.24
Tourism Geographies	Routledge	US 1999	2	2.617 (Q1)	2.63 (Q1)	5.73
Journal of Vacation Marketing	SAGE Publications	UK 1994	1	1.320 (Q1)	1.03 (Q1)	6.43

From the information included in the Scimago Journal Ranking database, all the journals are in the first quartile. Of the journals that accumulate the most papers Tourism Management has an impact factor in third place and is fourth in total citations; Journal of Travel Research is second in impact factor and seventh in total citations, while Journal of Destination Marketing & Management is eighth in the ranking and tenth in total citations.

3.2. Scientific production

Research findings are generally disseminated through the number of published articles, which indicate progress in a specific scientific field. The field exhibits strong academic impact, with high citation rates and contributions from diverse authors and sources.

The annual growth rate of publications in this field is 6.76%, indicating a steady increase in academic interest over time. Figure 2 illustrates an overall upward trend in scientific production from 2010 to 2024, albeit with notable fluctuations. The first significant increase occurred in 2013, with eight articles published, following three years of modest output of one or two articles annually. Subsequently, a slight decline is observed during 2014-2016, with four or five publications per year. The year 2017 marks another notable uptick in production. However, the peak was reached in 2021 with twelve publications, followed by a considerably less prolific 2022 regarding tourism publications in this field.

Figure 2
Annual scientific production

This decline may reflect disruptions in tourism research following the COVID-19 pandemic, especially in the tourism sector, which peaked globally in 2020 and 2021. Many researchers and institutions experienced delays in publishing, conducting fieldwork, accessing data, and finalizing manuscripts. Furthermore, priorities may have shifted from the academic topics analyzed towards more immediate and crisis-driven topics, such as resilience, recovery, and crisis management, rather than focusing on destination image. Therefore, we understand that this decline should be seen more as a temporary consequence of the context than as a permanent loss of academic interest in destination image research.

An analysis of scientific production, based on the countries of affiliation of the authors, reveals that research output is predominantly concentrated in a few leading nations, as shown in Table 5. China leads in citations, followed by the USA, Australia, UK and South Korea, indicating strong academic contributions from these regions.

Table 5
Scientific production by authors’ countries

Country	Freq.
China	70
USA	44
Australia; UK	19
South Korea	17
Spain	10
Malaysia	9
Turkey	7
Germany; Greece; South Africa;	5
Finland; France; Iran; Israel; Italy; New Zealand; Portugal	4
Austria; Denmark;	3
Belgium; Canada; Chile; Lebanon; Mexico; Norway; Pakistan; Sweden; Vietnam	2
Jordan; Poland; Russia; Singapore	1

3.3. Authors and organization

This bibliometric analysis identified a total of 220 authors, with only three single-author documents, as presented in Table 6. On average, each document includes 3.16 co-authors, highlighting the strong emphasis on teamwork within this research area. Additionally, 52.63% of the documents feature international co-authorship, further under-scoring the global scope and collaborative nature of research in this field.

Table 6
Authors with a higher number of publications

Authors	Articles	Articles Fractionalized
Al-Ansi Amr	3	0.92
Han Heesup	3	0.92
Stylidis Dimitros	3	0.92

Interestingly, more than 92% of authors in our dataset produced only one publication, while a smaller group has published three articles, as detailed in Table 7. This finding suggests that expertise in destination image is highly concentrated among a few scholars, each with low production output, with a maximum of three articles.

Table 7
Frequency of number of publications per author

Number of Articles	Number of Authors	Frequency
1	202	0.927
2	13	0.059
3	3	0.014

3.4. Document contents

The word frequency analysis, represented in Figure 3 by a word cloud, provides a clear picture of the main themes and concepts explored in the analyzed literature. Terms such as “satisfaction” (34 times) and “model” (27 times) are the most frequently mentioned, indicating that much of the research in this area focuses on conceptual models and customer satisfaction. Another recurring theme is “behavioral intentions” (23), focusing on understanding tourists' behavior and attitudes. Furthermore, concepts such as “perceptions” (20), “loyalty” (17), “image” (16), and “destination image” (14) emphasize brand loyalty and destination perceptions. Less common but still notable terms such as “authenticity,” “service quality,” “location connection,” and “experience” reflect vital factors that influence tourists' behavior and decision-making processes.

Figure 3
Word cloud based on the most prevalent keywords

The strategic thematic map provides a two-dimensional visualization of topic development within the dataset. This visualization is based on the centrality and density of the keywords in the analyzed data. Centrality (x-axis) reflects the relevance of a given topic to the overall development of the research field. At the same time, density (y-axis) identifies that topic's degree of internal development (Cobo et al., 2011). The thematic map presented in Figure 4 was generated by Biblioshiny, based on 322 keyword units.

Figure 4
Thematic map of author’s keywords

Basic themes in the lower-right quadrant are identified as central topics to the field but remain relatively underdeveloped. The most prominent cluster in this analysis falls within this category and can be labeled as “satisfaction,” which includes terms such as “satisfaction” (34 occurrences), “model” (27), and “behavioral intentions” (23). These results indicate the strong historical focus of tourism research on explaining tourists’ post-visit responses through satisfaction-based models. Although these themes are central to understanding the DI–BI relationship, further work could explore how these constructs interact with emerging factors such as digital engagement, co-creation experiences, and technology-mediated tourism interactions.

Clusters located in the upper-right quadrant are considered motor themes, as they exhibit both high centrality and high density. The “trust” cluster illustrates this category, highlighting the increasing importance of trust-related constructs in tourism research. Keywords such as “trust,” “country image,” and “congruence” emphasize the role of credibility, authenticity, and destination–tourist congruence in shaping tourists’ behavioral responses. This trend demonstrates the growing relevance of relational and psychological factors in the formation of DI and its influence on BI, particularly in contexts where tourists rely heavily on digital information and online interactions.

Niche themes appear in the upper-left quadrant and represent highly specialized topics with strong internal development but limited connections to the broader research field. The cluster related to “brand value” reflects the application of branding perspectives within tourism research. Terms such as “brand equity,” “purchase,” and “empirical test” indicate a focus on consumer-oriented approaches derived from marketing literature. While these themes are conceptually well developed, their integration with the broader DI–BI framework remains limited.

The lower-left quadrant contains themes with low centrality and density, which typically represent emerging or declining topics. In this analysis, the cluster including keywords such as “attitudes,” “information,” and “photos” appears in this category. These topics point to increasing interest in how tourists’ perceptions are shaped by the information they consume, particularly through visual and digital media. Considering the rapid digital transformation of tourism, these themes may represent emerging directions in the study of how digital content influences destination image and subsequent BI.

Table 8 provides a comprehensive overview of studies that explore how destination image (DI) and other related variables influence various aspects of tourist behavior, such as travel intentions, behavioral intentions, place attachment, satisfaction, and loyalty. The table is organized chronologically and categorizes the studies based on three distinct phases of the tourist experience: pre-trip, during-trip, and post-trip. Each study highlights the independent and dependent variables investigated, as well as the positive path relations identified in their analyses.

Table 8
Summary of studies analyzed the influence of the destination image on tourist behavior

Author (Year)	Independent variables	Dependent variable	Positive path relations containing variables of image
PRE - TRIP
Josiassen & Assaf (2013)	DI, SA, NS.	Travel decision (TD)	DI+SA+NS→TS
Al-Kwifi (2015)	DI, ATI	Intention to visit (INT)	DI→ATI→INT
Fu et al. (2016)	DI1, DI2, PI.	Travel intention (TI)	PI→DI1→TI; PI→DI2→TI PI→DI1→DI2→TI; PI→TI
Choi et al. (2018)	IQ, DI2, DI1	Intention to visit (INT)	IQ→DI2; IQ→DI1; IQ→INT; DI2→DI1; DI2→INT; DI1→INT
Chaulagain et al. (2019)	CI, DI, DF	Intention to visit (INT)	CI→DI; CI→INT; DI→INT
Lai et al. (2020)	DI6, DI7	Intention to visit (INT)	DI6→INT; DI6→DI7→INT
Lin et al. (2020)	DI, VR, PN, HN	Travel intention (TI) Slow Travel Intention - Slow Travel Experience (STI_1) Environmental Concerns (STI_2)	DI→TI; VR→STI_1; VR→STI_2; PN→STI_1; PN→ STI_2; HN→STI_1; HN→ STI_2; VR→ PN; VR→HN.
Luong (2023)	EDI, ATTE, BEL, BIO	Ecotourism intention (EINT)	EDI→BEL; EDI→ATTE, EDI→EINT
Qiu & Zuo (2023)	CICHL, EICHL, DI	Behavioral intention (BI)	CICHL→BI; CICHL→EICHL; EICHL→BI; CICHL→DI; EICHL→DI; DI→BI
DURING - TRIP
Deng & Li (2014)	EI, DI	Overall attitude toward the destination (OA) Behavioral intention (BI)	EI→DI; DI→BI; OA→BI; EI→OA
Jiang et al. (2017)	DI, AUT	Place attachment (PA)	DI→AUT→PA
Liu et al. (2020)	DI2, ABL, BA, PCD	Place attachment (PA)	ABL→DI2; ABL+BA→DI2; ABL→DI2→PA
POST - TRIP
Kim (2012)	DI, TE	Intention to revisit (INTR)	TE→DI→INTR
Assaker & Hallak (2013)	DI, S, NS	Intention to revisit (INTR)	NS→DI→S→INTR
Chew & Jahari (2014)	DI1, DI2, PR2, PR3	Intention to revisit (INTR)	RSP→CI→INTR; RSP→CI→INTR; RF→CI→INTR; RF→AI→INTR
De Nisco et al. (2015)	CI1, CI2, S, DI	Post-visit intentions (INTR) Post-visit intentions toward the national products (IRVP)	S→CI1; S→CI2; S→DI; CI1→CI2; CI1→DI; CI2→DI; CI2→INTR; CI2→IRVP; DI→INTR; DI→IRVP
Hallmann et al. (2015)	DI1, DI2, DI4	Intention to revisit (INTR)	A1+A2→A4→INTR
Kim et al. (2015)	DI, S, PV, PQ	Intention to revisit (INTR)	DI→S→PV→INTR
Papadimitriou et al. (2015)	DP, DI2, DI4	Intention to revisit (INTR) Intention to recommend (IR)	DP→DI4; AI→DI4; DI4→INTR; DI4→IR
Stylos et al (2016)	DI1, DI2, DI3, DI4, PNB	Intention to revisit (INTR)	DI1→INTR; DI2→INTR; PNB→DI4→INTR
Stylos et al (2017)	DI1, DI2, DI3, DI4, PA	Intention to revisit (INTR)	DI1→DI4; DI2→DI4; DI3→INTR; DI4→INTR; DI1→DI4→INTR; DI2→DI4→INTR; DI3→DI4→INTR
Souiden et al. (2017)	DI, DP1, DP2, ATT, BI	Behavioral Intentions (BI) Intention to revisit (INTR) Intention to recommend (IR)	DI→DP1; DI→DP2; DP1→AT; DP2→AT; AT→BI.
Choe & Kim (2018)	ATLF, FDI	Intention to recommend local food (WOMF) Intention to visit the destination for food tourism (INTF)	ATLF→FDI; ATLF→WOMF; ATLF→ INTF; FDI→WOMF; FDI→INTF
Han et al. (2019)	HFA, DI2, DI4	Revisit intention (INTR) Recommendation intention (IR)	HFA→DI2; HFA→DI4; DI2→DI4, DI2→INTR; DI2→IR; DI4→INTR; DI4→IR
Campo-Martínez et al. (2020)	DS, DI4, PE	Intention to revisit (INTR)	S→INTR; DI4→INTR; PE→INTR (With partner and with Friends) S→INTR; PE→INTR (As a family with children)
Tavitiyaman et al. (2021)	SI, IM, SS, ES, IT, SF, DI	Behavioral Intentions (BI) Say positive things (INTR_1) Recommend to others (INTR_2) Encourage friends to visit (INTR_3)	SI→DI, SS→DI, ES→DI, SF→DI, DI→BI
PRE - TRIP
Rasoolimanesh et al. (2021b)	VE, AUT, DI, MTEs	Revisit Intention (INTR) eWOM intention (eWOMINT)	VE→MTEs; AUT→MTEs; DI→MTEs; MTEs→eEWOMINT; MTEs→INTR; VE→ eEWOMINT; VE→INTR; DI→ eEWOMINT; AUT→MTEs→ eEWOMINT; DI→MTEs→ eEWOMINT, VE→MTEs→INTR, AUT→MTEs→INTR, DI→MTEs→ INTR
Zhang et al. (2018)	CI, DI, MTEs	Revisit Intention (INTR).	CI→DI, DI→MTEs, CI→MTEs, MTEs→INTR.
Prayag et al. (2015)	J, LO, PS, DI4, S	Intention to recommend (IR)	J→DI4; LO→DI4; PS→S; DI4→S; DI4→IR; S→IR
Stylidis et al. (2017)	DI1, DI2, DI4	Intention to recommend (IR)	DI1→DI2; DI1→DI4→INT; DI2→DI4→INT; DI1→INT; DI2→INT
Soltani et al. (2020)	TLFCV, TLFEV, SMIs, ATLF, FDI	Behavior intention (BI) Intention to recommend food (IRF) Intention to revisit the destination for food tourism (INTRF)	TLFCV→ATLF, TLFCV→FDI, TLFEV→ATLF, TLFEV→FDI, SMIs→ATLF, SMIs→FDI, ATLF→BI, FDI→BI
Le & Hsu (2024)	PCD, PD, DI1, DI2	Behavior intention (BI) Recommend intention (IR) Say positive things about (SPOS) Revisit intention (INTR)	PCD→DI1; PCD→DI2; PD→DI1; PD→DI2; DI1→BI; DI2→BI
Veasna et al. (2013)	DSC, DI, PA	Satisfaction (S)	DSC→DI→PA→S
Lam, et al. (2020)	PU, PEU, AES, HOM, PCE, DI1, DI2	Satisfaction (S)	PU→PCE; PEU→PCE; AES→PCE; HOM→PCE; PCE→DI1, PCE→DI2; DI1→DI2; DI1→S; DI2→S
Prayag & Ryan (2012)	DI, PI, PA, S	Loyalty (L) Revisit Intentions (INTR) Recommendation Intentions (IR)	PI→DI→PA→S→INTR+IR; PI→DI→S→INTR+IR
Chen & Phou (2013)	DI, DP, S, DT, PA	Loyalty (L)	DI→DP→S→L; DI→DP→S→DT→L DI→DP→DT→L; DI→S→DT→L DI→S→DT→PA→L; DI→DT→L DI→DT→PA→L
Sun et al. (2013)	DF, D1; PV, S	Loyalty (L) Intention to revisit (INTR) Willingness to recommend (IR)	F→DI→PV→S→INTR+IR; F→DI→S→INTR+IR
Gursoy et al. (2014)	PI, PA, DI, PQ, S	Loyalty (L)	PI→DI; PA→DI; DI→PQ; PQ→S; PQ→L, S→DL
Zhang et al. (2014)	DI1, DI2, DI4, CI-AI, S-C	Loyalty (L) Attitudinal Loyalty (Intention to recommend- IR Behavioral Loyalty (Visit & Revisit intention- INT/INTR) Composite Loyalty (Behavioral intention- BI).	CI→WOM, CI→VRI, CI→BI, AI→WOM, AI→VRI, AI→BI, OI→WOM, OI→VRI, OI→BI, CI-AI→WOM, CI-AI→VRI; S-C→VRI, S-C→BI.
Kim (2018)	MTEs, DI, S	Loyalty (L) Revisit intention (INTR) Word of mouth intention (WOMINT)	MTEs→DI; DI→INTR; DI→WOMINT; DI→S; MTEs→DI→S→INTR+WOMINT
Lv et al. (2020)	DI, PQ, PV, SI, S	Loyalty (L) Intention to revisit (INTR) Willingness to recommend (IR)	DI→INTR+IR; DI→S→INTR+IR; DI→PV→INTR+IR; DI→PQ→INTR+IR; SI→INTR+IR; SI→S→INTR+IR; SI→PV→INTR+IR; SI→PQ→INTR+IR
Li et al. (2021)	E, DI, S	Loyalty (L) Ecotourism loyalty (EL)	E→DI; E→S, DI→S; S→L; E→L; DI→L; S→EL; E→EL; DI→EL
Marques et al. (2021)	DI1, DI2, DI5, S	Loyalty (L) Willingness to recommend (IR) Post-visit intentions toward the national products (IRVP)	DI1→IR→IRVP; DI2→S; S→IR→IRVP; DI2→IR→IRVP; DI5→IRVP
Tasci et al. (2022)	DI1, DI2, PA1, PA2, PCD	Loyalty (L) Intention to revisit (INTR) Willingness to recommend (IR)	DI1+DI2→PA1+PA2; PA1+PA2→INTR+IR; DI1+DI2→PA1+PA2→INTR+IR

• Source: compiled from main studies. Note: The meaning of independent variables symbols: Destination image (DI), Food destination image (FDI), Cognitive image (DI1), Affective image (DI2), Conative Image(DI3), Overall image (DI4), Unique image (DI5), Cognitive food image (DI6), Affective food image (DI7); Social awareness (SA); Normative sensitivity (NS); Attitude toward the images (ATI); Personal involvement (PI); Information credibility (IQ); Country image (CI), Cognitive country image(CI1), Affective country image(CI2); Destination familiarity(DF); Destination trust (DT); Virtual reality effects (VR); Personal Nostalgia (PN); Historical Nostalgia (HN); Eco-destination image (EDI); Ecotourism attitudes (ATT); Environmental beliefs (BEL); Biospheric values (BIO); Cognition of ICH label (CICHL), Emotion of ICH label (EICHL); Event Image (EI); Perceived authenticity (AUT); Affective brand loyalty (ABL); Brand authenticity (BA); Perceived cultural distance (PCD); Travel experience (TE); Satisfaction (S); Novelty-seeking motivation (NS); Psychosocial risk (RSP); Financial risk (RF); Perceived value (PV), Perceived quality (PQ); Personal Normative Beliefs (PNB); Destination personality(DP), Destination personality (DP1: Sentimental), Destination personality (DP2: Competence); Attitude (ATT); Attitude toward local food (ATLF);); Halal-friendly destination attributes (HFA); Prior experience(PE); Smart inf. system (SI); Intelligence tourism mgt (IM) Smart sightseeing (SS); E-commerce system (ES); Intelligent traffic (IT); Smart forecasting (SF); Visitor engagement(VE); Tourism Experiences (ET); Memorable Tourism Experiences (MTEs); Joy (J); Love(LO); Positive surprise (PS); Local food consumptions value (TLFCV); Local food Experimental value (TLFEV); Social Media Influencers(SMIs); Attitudes Toward Local Food (ATLF); Perceived discrimination (PD); Destination Source Credibility (DSC); Perceived usefulness (PU); Perceived ease of use (PEU); Aesthetic (AES); Homophily (HOM); Plataforma co-creation experience (PCE); Overall satisfaction (S); Perceived service quality (PQ); Cognitive-affective joint image (CI-AI); Self-congruity (S-C); Sensory impressions (SI); Experiences (E); Place attachment (PA), Place identity (PA1), Place dependence (PA2).

A cross-stage comparison of the studies summarized in Table 8 reveals several consistent patterns. First, mediating variables such as satisfaction and place attachment emerge as central mechanisms linking destination image to behavioral intentions, particularly in the during- and post-trip phases. Second, the structure of the DI–BI relationship varies across stages: cognitive evaluations dominate the pre-trip phase, emotional and experiential factors become more prominent during the trip, and evaluative and loyalty-related outcomes prevail in the post-trip stage.

These findings indicate that the DI–BI relationship operates through distinct but interconnected mechanisms that evolve across the tourist journey, reinforcing the need for a stage-based and process-oriented understanding of how destination image influences behavioral intentions.

In the pre-trip phase, the studies analyzed primarily examine the influence of destination image (DI) on visit intention (INT) and travel decision-making (TD). This relationship represents one of the most established relationships in literature. Several studies show that a positive destination image significantly increases tourists’ intention to visit a destination, highlighting the importance of maintaining a strong and favorable image to attract potential tourists (Al-Kwifi, 2015; Chaulagain et al., 2019; Qiu & Zuo, 2023).

Beyond this direct relationship, the literature also identifies mediating mechanisms that explain how DI influences behavioral intentions. For example, Fu, Ye, and Xiang (2016) and Choi et al. (2018) demonstrate that cognitive image (DI1) and affective image (DI2) mediate the influence of factors such as information credibility (IQ) and personal involvement (PI) on travel intention. These findings indicate that both rational evaluations and emotional perceptions play a critical role in shaping tourists’ decision-making before travel.

Some studies further show that the overall image of a country can influence the perceived image of specific destinations within that country (Chaulagain et al., 2019; Qiu & Zou, 2023). This suggests that tourism promotion strategies should consider both national and local branding, particularly for destinations that depend on national reputation to attract international visitors.

During the travel stage, the literature emphasizes the role of destination image in shaping tourists’ emotional experiences and connections with the destination. In particular, studies focus on how DI influences place attachment (PA) and subsequent behavioral intentions.

Research by Jiang et al. (2017) shows that destination image influences existential authenticity (AUT), which subsequently strengthens place attachment. Similarly, Liu et al. (2020) find that brand authenticity (BA) and affective brand loyalty (ABL) influence affective destination image (DI2), which in turn reinforces place attachment. These findings demonstrate that emotional responses and perceived authenticity during the travel experience are crucial for fostering stronger connections between tourists and destinations.

Other studies highlight the role of event image (EI) in shaping destination perceptions. Deng and Li (2014) show that event image can influence destination image, which subsequently affects tourists’ behavioral intentions and overall attitudes toward the destination. This relationship is particularly relevant for destinations that rely on events to strengthen their attractiveness and stimulate revisit and recommendation behaviors.

In recent years, the effects of destination image on travelers’ post-trip behavior have been studied more deeply by academics compared to pre-trip and on-trip behaviors. Research in this phase focuses primarily on revisit intention, recommendation intention, satisfaction, and loyalty, which represent the most common outcomes of a positive destination image. Numerous studies demonstrate that a favorable destination image directly increases tourists’ revisit intentions (Kim, 2012; Assaker & Hallak, 2013; De Nisco et al., 2015; Han et al., 2019) and recommendation intentions (Papadimitriou et al., 2015; Stylidis et al., 2017). These results indicate that tourists who hold positive perceptions of a destination are more likely not only to return but also to share their experiences with others.

A further recurring pattern in the literature is the mediating role of satisfaction, which significantly influences loyalty and behavioral intentions following the travel experience. Studies such as Kim et al. (2015), Lv et al. (2020), and Marques et al. (2021) show that positive post-visit perceptions significantly increase tourist satisfaction, which subsequently influences loyalty and behavioral intentions. In many cases, satisfaction acts as a key mediator between destination image and revisit or recommendation intentions (Assaker & Hallak, 2013; Chen & Phou, 2013; Li et al., 2021).

Research also highlights the importance of the cognitive and affective components of DI in shaping post-visit behavior. Studies by Stylos et al. (2017) and Marques et al. (2021) demonstrate that both dimensions jointly influence satisfaction, loyalty, and recommendation intentions, indicating that tourists’ rational evaluations and emotional experiences together determine their long-term relationship with a destination.

Additional factors such as memorable tourism experiences (MTEs), authenticity, and place attachment also play an important role in post-trip behavioral outcomes. Studies by Kim (2018) and Zhang et al. (2018) show that memorable experiences strengthen destination image and increase revisit and recommendation intentions. Similarly, Souiden et al. (2017) and Rasoolimanesh et al. (2021a) highlight the importance of authenticity and place attachment in fostering loyal tourist behavior.

Nevertheless, it is important to clarify that Table 8 summarizes the positive relationships identified between DI variables and BI outcomes, while the literature indicates that the DI–BI relationship involves multiple interacting mechanisms and variations in its effects. Although many studies report direct relationships, others show that the influence of DI on behavioral intentions often operates indirectly through mediating variables such as satisfaction, place attachment, authenticity, and trust. These variations suggest that the impact of destination image is contingent on contextual, experiential, and psychological factors.

From a broader perspective, the variables identified in literature can be classified into four main categories. Antecedents include factors such as information credibility, personal involvement, event image, country image, and memorable tourism experiences, which contribute to shaping destination image before or during travel. Mediators frequently identify in the literature include satisfaction, place attachment, authenticity, and trust, which explain how destination image translates into behavioral intentions. Moderators include contextual and individual factors such as tourist experience, cultural background, information sources, and destination type, which influence the strength of the DI–BI relationship. Finally, the most common consequences of a positive destination image are revisit intention, recommendation intention, loyalty, and word-of-mouth, confirming the central role of DI as a predictor of tourist behavior.

These stage-specific differences reinforce the dynamic nature of the DI–BI relationship, highlighting how its underlying mechanisms evolve throughout the tourist journey. Rather than operating as a static association, destination image influences behavioral intentions through context-dependent processes that vary across the pre-trip, during-trip, and post-trip phases.

The empirical findings identified in this review align with several theoretical frameworks discussed in the literature. The relationship between destination image and behavioral intentions is consistent with the Theory of Planned Behavior (Ajzen, 1991), which emphasizes the role of attitudes in shaping behavioral intentions. Similarly, the mediating role of satisfaction corresponds with Expectation–Confirmation Theory (Oliver & Swan, 1989), which explains how post-consumption evaluations influence future behavioral responses. Together, these theoretical perspectives support the interpretation of the DI–BI relationship as a dynamic process in which cognitive perceptions, emotional responses, and post-consumption evaluations interact throughout the travel experience.

The analysis also highlights several promising directions for future research. Greater attention should be given to digital innovation and emerging technologies, including social media, artificial intelligence, and immersive technologies, which increasingly shape tourists’ perceptions of destinations. Further research could also examine changes in tourist behavior, particularly in relation to co-creation experiences and digital engagement. Longitudinal studies would help clarify how destination image evolves over time and influences behavioral intentions across different travel stages. In addition, negative destination perceptions remain relatively underexamined in literature and deserve further exploration.

Future studies could also expand the bibliometric scope by examining the relative proportion of DI–BI research within tourism journals, as well as exploring country-level differences by considering contextual factors such as research policies, academic networks, and market characteristics. Moreover, applying advanced bibliometric techniques, such as co-citation analysis and collaboration network analysis, would allow the identification of research clusters and schools of thought, providing a deeper understanding of the intellectual structure of the field. Finally, multi-method research designs that combine quantitative and qualitative approaches may provide deeper insights into the complex dynamics of the DI–BI relationship.

4 CONCLUSIONS

This study proposes an integrative framework that conceptualizes the relationship between destination image (DI) and tourists’ behavioral intentions (BI) as a dynamic, stage-dependent process across the tourist journey. By synthesizing evidence from the literature, the framework integrates antecedents, mediators, and outcomes within a unified structure that captures how cognitive, affective, and evaluative mechanisms interact over time, thereby contributing to a process-oriented and stage-based theoretical understanding of the DI–BI relationship.

Unlike previous reviews, which have predominantly examined isolated relationships, specific behavioral outcomes, or limited stages of the travel experience, this study advances the literature by offering a comprehensive and temporally structured perspective of the DI–BI relationship. By explicitly linking the pre-trip, during-trip, and post-trip phases, this framework provides a more systematic understanding of how destination image influences behavioral intentions through interconnected processes rather than fragmented associations.

From a theoretical perspective, the analysis also shows that the relationship between DI and BI is not always direct, as it often operates through mediating mechanisms such as satisfaction, place attachment, authenticity, and memorable tourism experiences. From a managerial perspective, the findings highlight the need for Destination Marketing Organizations (DMOs) to manage destination image strategically throughout the tourist journey. Strengthening cognitive and affective perceptions before travel, facilitating authentic experiences during the visit, and encouraging positive post-trip engagement are key elements for fostering tourist loyalty and positive word-of-mouth.

The study also identifies several avenues for future research, particularly regarding the role of digital technologies and immersive experiences, differences among tourist segments, and the need for longitudinal and multi-method approaches to better capture the dynamic nature of destination image. Beyond managerial implications, the results also have social relevance, as effective destination image management can support more sustainable tourism development by promoting authentic experiences and strengthening connections between tourists and local communities.

• Como Citar:
  Más, R. J. L., & Díaz-Contino, C. G. (2026). Destination image and its effect on tourist behavioral intentions: systematic review. Revista Brasileira de Pesquisa em Turismo, São Paulo, 20, e-3259, 2026. https://doi.org/10.7784/rbtur.v20.3259
• Revisado em pares.

Declaration of Data Availability

Data should be requested from the author via email: [e-mail]

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Edited by

• Editor
  Thiago de Luca Sant'Ana Ribeiro.

Publication Dates

• Publication in this collection
  03 Aug 2026
• Date of issue
  2026

History

• Received
  17 July 2025
• Accepted
  31 Mar 2026