Destination image: A review from 2012 to 2023

Abstract

More than 50 years have passed since the first article on destination image was published, and this body of research is still one of the most popular topics in tourism studies. However, limited research has focused on recent advancements and emerging trends in destination image research. Therefore, there is a pressing need for a comprehensive and up-to-date review to evaluate its progress, identify key features, and explore future research possibilities. Employing a meta-analysis methodology, this study scrutinized 178 articles from 11 esteemed academic journals published between January 2012 and April 2023. The analysis encompasses various aspects, including the publication trends, journal distribution, productive authors, research topics and research methods. Furthermore, this study introduces the concept of “e-WOM image,” which offers a profound understanding of the intricate associations among various topics within the domain of destination image research. The exploration of “e-WOM image” as a potential research field holds promise for advancing the field of destination image research in the coming years.

Keywords:

• destination image
• literature review
• projected image
• perceived image
• e-WOM image
• UGC

Public interest statement

The paper titled “Destination Image: A Review from 2012 to 2023” addresses the need for a comprehensive and contemporary evaluation of destination image research. By conducting a meta-analysis of 178 articles from esteemed academic journals, this study offers insights into publication trends, productive authors, research topics, and research methods. Notably, this study introduces the concept of “e-WOM image,” which provides a deep understanding of the intricate relationships within destination image research. This exploration opens doors to a potential research field that holds promise for advancing the understanding of destination image. The findings of this study serve to enhance knowledge in the field of destination image, identify research gaps, and offer valuable research directions for future scholars. Moreover, the results hold significant implications for tourism destination marketing organizations, providing valuable insights to improve destination marketing strategies.

1. Introduction

Consumer decisions on destinations are influenced by various factors. Image is the decisive factor that affects consumers’ choice among various information (Levy, 1978).The concept of destination image was initially introduced to the field of tourism by Hunt (1971) during the early 1970s, marking its inception as a significant research area (Stepchenkova & Morrison, 2008). After decades of popularity, it now plays an important role in tourism and related research and has been widely studied (Stylidis, 2022).

During the early stages of research on destination image, scholars such as Crompton (1979), Gartner (1989), and Echtner and Ritchie (2003) examined the internal structure and attributes of destination image. Their contributions not only advanced the understanding of destination image but also laid the groundwork for research on measuring destinations. Concurrently, the process of destination image formation was explored by Gunn Clare (1972), Fakeye and Crompton (1991) and Gartner (1994), who emphasized the distinct characteristics associated with each stage in the formation process. Since then, the study of factors influencing the formation of destination image has gained considerable prominence (Baloglu & McCleary, 1999a; Tasci & Gartner, 2007). Influencing factors encompass a range of elements, including tourists’ cultural background, beliefs, personality traits, and social networks, as well as the destination’s political environment, sports events, popular culture, brands, commodities, and more (Eid et al., 2019; Elliot et al., 2010; C.-K. Lee et al., 2005; MacKay & Fesenmaier, 2000; Seo & Oh, 2017; Stylos et al., 2016). In light of the expanding research on these factors, researchers have paid particular attention to the role of media influence. This attention stems from the fact that potential tourists primarily rely on various media channels for obtaining destination information (Stepchenkova & Eales, 2010). Apart from conventional mass media, the increasing prevalence of social media platforms has contributed to the emergence of user-generated content (UGC) as a valuable data source for examining destination image (Daugherty et al., 2008). Textual descriptions, photos, and location information shared by social media users during and after their trips are employed to analyze the perceived image of the destination (J.-S. Lee & Park, 2023; Marine-Roig & Huertas, 2020; Tseng et al., 2015).

The research landscape of destination image is characterized by a wide array of topics, encompassing its conceptualization, measurement, factors, and the mediating effect of destination image on tourist intention, behaviour, and satisfaction (Ferrer-Rosell & Marine-Roig, 2020; K. Kim et al., 2012; Lai & Li, 2015; Maghrifani et al., 2021; Marine-Roig, 2021). The substantial attention given to destination image in research can be attributed to its important role as a predictor of tourists’ destination choice and travel behavior (Baloglu, 2000). A positive destination image enhances tourists’ inclination to evaluate a destination positively, thereby strengthening their willingness to revisit and recommend it (Bigne et al., 2001; De Nisco et al., 2015). The value of destination image extends to the branding of a destination, prompting DMOs to utilize the insights gained from destination image research to enhance destination management, promote tourist satisfaction, and foster loyalty (Echtner & Ritchie, 2003; Tasci et al., 2007).

Despite the significance of destination image as a fundamental domain in tourism research, there remains a limited number of review papers that specifically focus on this topic. The majority of recent literature reviews have primarily concentrated on specific subfields within destination image, thus failing to provide a comprehensive analysis of research characteristics and methodologies from a broader perspective. Given the active nature of destination image research field under consideration, undertaking a systematic review of the most recent research findings becomes imperative. As J and Andrew (2009) explained, the purpose of literature review methods is to ascertain existing work, evaluate the current state of knowledge, prevent redundancy, identify omissions or gaps, and pinpoint research priorities and necessary adjustments in research methodology. The significance and innovation of this study are reflected in three aspects. Firstly, it conducts a systematic review of the latest research on destination image, employing a meta-analysis approach that combines qualitative and quantitative methods. This comprehensive analysis could effectively address the existing research gap in the field. Secondly, this study selects samples from the most reputable journals in the field of tourism and hospitality research, ensuring that the chosen papers well represent the research characteristics of this area. Thirdly, a framework for research topics and interrelationships on destination image is proposed, and the temporal trends in the popularity of each research topic is uncovered. By doing so, it provides insights into potential research directions for the future. The ultimate objectives of this research include:

• To conduct an analysis of the research characteristics and trends present in selected publications.

• To classify research topics, explore their interrelationships, and reveal potential research opportunities.

• To identify gaps in research methods and provide recommendations for future scholars.

2. Literature review

2.1. Destination image

The definition of destination image proposed by Crompton in 1979 was widely used in the early days of destination image research. Crompton (1979) considered destination image as the sum of beliefs, ideas and impressions that a person has of a destination. Later, scholars gradually realised that destination image is a complex concept. As destination image is a highly abstract concept with the characteristics of comprehensiveness, diversity, relativity and dynamics, this concept lacks a unified definition. Echtner and Ritchie (2003) reviewed definitions used by previous destination image researchers and proposed six components of destination image which can be categorized into three groups (Functional-Psychological, Attributes-Holistic, Common-Unique). This conceptual framework provided ideas for measuring the characteristics of destination image from three dimensions. In the research of Gartner (1994), destination image was composed of three components which include cognitive image, affective image and conative image. Cognitive image refers to tourists’ perception of the attributes or characteristics of the destination, including tourist attractions, environment, public service and other infrastructure. Meanwhile, affective image refers to tourists’ cognition of a destination based on personal attitudes and values, whereas conative image is the tourists’ intention or the possibility of visiting a destination, equivalent to travel tendency. This framework of destination image has been widely applied and further expanded in many subsequent studies (Agapito et al., 2013; Steven; Pike & Ryan, 2004; Stepchenkova & Mills, 2010; Woosnam et al., 2020).

Bramwell and Rawding (1996) introduced another perspective to understand the destination image, distinguishing it into two distinct dimensions: projected image and perceived image. The image that tourism marketing organizations (DMOs) try to establish and promote in travel markets through various media channels is called the projection image, whereas the image formed in the mind of tourists or potential tourists, including impression, cognition and feeling, is called the perceived image (Andreu et al., 2000; Choi et al., 2007; Govers & Go, 2004). Despite the fact that the projected image plays a pivotal role in shaping the perceived image, there is a significant difference between these two images. This gap is attributed to various sociodemographic factors (Echtner & Ritchie, 2003).

On the basis of these three important destination image components models, referring to the study of Tasci et al. (2007), the authors integrate the conceptual framework of destination image as shown in Figure 1. The concept of destination image can be analyzed through two distinct aspects: the projected image and the perceived image. The perceived image mainly represents the holistic impression held by tourists towards a destination, encompassing three fundamental components: cognitive image, affective image, and conative image. Furthermore, destination attributes can be comprehended through two dimensions: common characteristics and unique characteristics, as well as functional characteristics and psychological characteristics. Functional characteristics relate to tangible aspects of the destination, while psychological characteristics pertain to intangible aspects. The cognitive, affective, and conative image of tourists, along with the attributes of the destination, form a dynamic interactive system, wherein each element can act as both a cause and a consequence of changes, emphasizing the interconnected nature of these factors (Tasci et al., 2007). Obviously, the study of the perceived image occupies a central position in destination image research. Previous studies have revealed that academic literature places greater emphasis on analyzing the perceived image compared to the projected image (Picazo & Moreno-Gil, 2019).

Figure 1. Conceptual structure of destination image.

In the study of destination image, scholars not only focus on its definition and components but also pay significant attention to the process of its formation and the factors that influence this process. They strive to explain how destination image is shaped. In this context, the destination image refers specifically to the perceived image. Figure 2 presents some of the crucial findings derived from these scholarly pursuits. Gunn Clare (1972) proposed a framework in which destination images transition from organic images to induced images, and eventually to modified induced images. The organic image is formed through exposure to non-tourism-specific sources of information. This organic image then develops into an induced image, which is influenced by directed information from DMOs. While a destination has limited control over its organic image, it can significantly impact the modification of an induced image through promotional and publicity efforts. This highlights the importance of destination marketing in managing and influencing tourists’ perceptions and attitudes towards a particular location. Fakeye and Crompton (1991) build upon this formation process and define the final stage image as a complex image. This implies that upon visiting a destination, a tourist will develop a more intricate image based on actual experiences and contact with the area. H. Kim and Chen (2016) proposed three essential driving processes that contribute to the formation of destination image: schema-driven, data-driven, and experience-driven. While their model emphasizes the identification of key factors at different stages of the image formation process, it does not introduce any fundamental advancements.

Figure 2. Factors and formation process of destination image.

The formation of the destination image is influenced by a multitude of factors. Gartner (1994) categorized these factors as overt information, autonomous information, and organic information. Baloglu and McCleary (1999b) proposed two aspects: stimulus factors, which refer to external influences, and personal factors, which pertain to tourists’ internal characteristics. Tasci and Gartner (2007) analyzed the formation of destination image from the perspectives of supply and demand, highlighting the influence of destination-oriented information and perceiver characteristics. Building upon this research, Huang et al. (2021) introduced the consideration of environmental factors. The analyses conducted by the aforementioned scholars regarding the influencing factors in destination image formation validate the idea that the destination image, as depicted in Figure 1, is a result of the reciprocal interaction between tourists and the destination.

2.2. Reviews of destination image studies

Although there are abundant studies on destination image, only a few have systematically reviewed the research on destination image. For instance, Chon (1990) reviewed 23 articles and classified destination image research into eight topics: (1) the relationship between destination image and traveller satisfaction; (2) the relationship between destination image and traveller buying decision-making; (3) the formation or change of destination image through cross-national and cross-cultural contacts; (4) the formation of destination image; (5) the change of destination image; (6) assessment and measures of destination image; (7) destination image and environmental psychology and (8) destination image and tourism development. Echtner and Ritchie (2003) reviewed 15 papers from a conceptualisation and measurement perspective and evaluated the advantages and disadvantages of various methods of defining and measuring destination image. An overview of destination image components was presented, and a list of destination image attributes was provided.

Gallarza et al. (2002) reviewed 65 papers published between 1971 and 1999 and proposed a conceptual model of destination image, which demonstrated four features of destination image’s nature: complex, multiple, relativistic and dynamic. In their research, seven different topics were grouped, and the methods for measuring destination image, as well as the most common attributes used in destination image, were discussed. In the same year, Steve Pike (2002) identified 142 papers published between 1973 and 2000 and discussed some of the characteristics of destination image research, including the context, measurement methods, data analysis methods and research focus. Tasci et al. (2007) conducted a critical review that concluded that the definition and measurement of destination image were still vague, and the use of inappropriate measurements may lead to bias in most of the literature.

Stepchenkova and Mills (2010) discussed ten emerging trends in destination image studies with an analysis of 152 articles from 2000 to 2007. This article suggested that the comprehensive influence of tourist information sharing on potential tourists and DMOs’ projected image must be further studied. Meanwhile, Nghiêm-Phú (2014) reviewed 177 papers published between 2008 and 2012, providing the projection of destination image and various information sources that should be considered and the perceived image and projected image were separately discussed. J. J. Li et al. (2015) investigated 18 prominent studies on destination image research released from 1991 to 2011 and proposed that future research should take more into account the influence of cultural differences, destination types, gender of tourists and other factors on destination image.

The literature reviews mentioned above encompass studies published prior to 2012; however, there is a scarcity of literature reviews on destination image after that. A thorough examination of the Google Scholar database indicates that since 2019, only nine literature reviews have focused on this topic, with the majority of them concentrating on certain subfields. These subfields include attributes of destination image (Primananda et al., 2022), destination image and travel intention (Afshardoost & Eshaghi, 2020), the impact of destination image on travel behavior (Borlido & Kastenholz, 2021), destination image and gastronomy tourism (Sio et al., 2021), wine tourism destination image (Sekhniashvili, 2021), the influence of electronic word-of-mouth (e-WOM) on destination image formation (Khan et al., 2021; Yerizal & Abror, 2019), and a quantitative study on the antecedents of destination image (Yilmaz & Yilmaz, 2020). The only review paper with an overall analysis of the trend in destination image research comes from Huang et al. (2021), who examined 908 articles from 182 journals published between 1990 and 2019. However, this literature primarily relies on qualitative analysis, lacking quantitative descriptions, and does not provide a comprehensive review of the research topics and methods employed in the reviewed papers. Additionally, its focus is limited to literature published prior to 2019.

The advent of electronic media, virtual reality, electronic payment systems, and big data has brought about significant transformations in the formation process and factors of destination image, leading to increased complexity in tourists’ tourism behavior. Consequently, it becomes imperative to undertake a comprehensive literature review focusing on destination image in recent years. This review aims to unveil the distinctive characteristics and emerging trends in destination image research, while also identifying current gaps and exploring promising avenues for future investigation.

3. Methodology

This study employed a meta-analysis methodology, which systematically combines and analyzes data from multiple independent studies on a specific research topic (Davis et al., 2014). This approach involves a rigorous and comprehensive review of the existing literature, identifying relevant studies, extracting data, and synthesizing the findings to derive overall conclusions. Meta-analysis is widely recognized as an indispensable approach for conducting literature reviews (Snyder, 2019). In this study, the authors adhered to specific criteria to select samples and collect data from esteemed academic databases. A total of 178 articles from 11 renowned academic journals in the fields of tourism and hospitality, published between January 2012 and April 2023, were reviewed to disclose the characteristics and trends of destination image research. Following the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Haddaway et al., 2022), Figure 3 illustrates the flowchart depicting the data collection process.

Figure 3. Data collection process.

Firstly, the careful selection of an appropriate database for the paper search is of utmost importance (Bramer et al., 2017). Four prominent databases, namely ScienceDirect, Sage Journals Online, Taylor & Francis Online, and Emerald Insight, were specifically chosen for this study. These databases collectively encompass a wide range of academic journals that focus on tourism and hospitality research.

Secondly, to ensure the inclusion of high-quality articles in this review, emphasis was placed on leading academic journals, as certain journals hold a stronger reputation within the academic community (Hall, 2011). The selection of journals followed three criteria: (1) The top 20 journals were chosen based on their ranking in the field of Tourism, Leisure, and Hospitality Management according to Scimago Journal Rank (SJR) 2022. (2) The top 20 journals were selected based on their ranking in the category of Tourism and Hospitality Management according to Google Scholar Metrics 2022. (3) Journals with a rating of 2 or higher according to the ABS Academic Journal Guide 2021 were included. As a result of applying these criteria, a total of 11 journals met all three conditions simultaneously.

Next, a keyword search was conducted within these 11 journals. The keyword “destination image” was used to identify relevant research samples, with a focus on the abstract, keywords, or title of the articles. The search was limited to the timeframe of January 2012 to April 2023. The year 2012 was chosen as the starting point for data collection because most review literature on destination image studies focused on pre-2012 studies; however, recent literature could better highlight areas requiring further research (Guillet & Mohammed, 2015). The year 2023 was selected as the endpoint to incorporate the most recent academic articles in this research area. A total of 473 articles were initially screened during this phase.

Finally, the included articles underwent a thorough review to ensure they met the study’s requirements according to the following three conditions: (1) Only full-text research articles were retained, and any articles that were not in full-text format were excluded. (2) Only articles whose research topics focused on destination image were retained, while those with research topics unrelated to destination image were removed. (3) Duplicates were eliminated. By applying these criteria, the final set of articles met the necessary requirements for this study. As a result, a total of 178 articles were selected as samples, with each article carefully classified and examined. Table 1 provides an overview of the information regarding these 11 journals, including the number of articles selected from each journal.

Table 1. Overview of the selected journals

Selected Journals	Journal Rankings			Number of Selected Articles
	Scimago Journal Rank (SJR) ^a*	Google Scholar Metrics h5-index ^b*	ABS Academic Journal Guide ^c*
Tourism Management	3.561	122	4	38
Annals of Tourism Research	3.426	77	4	8
Journal of Travel Research	3.249	76	4	28
International Journal of Hospitality Management	2.928	106	3	1
International Journal of Contemporary Hospitality Management	2.5	86	3	4
Journal of Sustainable Tourism	2.966	72	3	7
Current Issues in Tourism	2.062	79	2	21
Tourism Management Perspectives	1.92	70	2	20
Journal of Travel & Tourism Marketing	2.126	60	2	34
Tourism Geographies	2.596	52	2	4
Journal of Hospitality & Tourism Research	1.357	47	2	13
Total				178

Note: ^a* Retrieved from https://www.scimagojr.com/journalrank.php?category=1409

^b*Retrieved from https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=bus_tourismhospitality

^c*Retrieved from https://charteredabs.org/academic-journal-guide-2021-available-now/

During the data analysis phase, the authors conducted descriptive statistics on various aspects of the sample, including the publication time, published journals, and author information. Particular attention was given to the research topic of each article, and detailed coding of sub-themes and research methods employed was performed. Thorough statistical analysis was conducted to provide comprehensive insights into the data.

4. Results

4.1. Trends in the number of publications

Based on the findings depicted in Figure 4, it is evident that the data exhibited substantial fluctuations, yet it did not demonstrate a notable upward or downward trend overall. In particular, there was a significant upsurge in destination image research during the year 2021, as supported by the highest number of published articles (24), closely followed by 2013 (23). This surge in research activity in 2021 could be attributed to the considerable impact of the COVID-19 pandemic on the global tourism industry in the preceding year, which garnered substantial attention from scholars. The considerable volume of research outcomes in 2013 May be attributed to the emergence of a prominent research area characterized by the rapid growth of social media. Specifically, scholars extensively examined various social dimensions, such as online comments and photos related to a destination, and their influential role in shaping the formation of the perceived image (Camprubí et al., 2013; Michaelidou et al., 2013; Stepchenkova & Zhan, 2013). Despite a decline in research output in 2022, a noticeable immediate recovery trend is anticipated in 2023, with the publication of 8 articles solely from January to April, equivalent to the cumulative output of the entire year 2022.

Figure 4. Publications by year.

4.2. Publications by journal and author

Among the selected journals, the Journal of Tourism Management held the largest share of articles, accounting for 21.3% of the total, followed by the Journal of Travel & Tourism Marketing (19.1%), Journal of Travel Research (15.7%), Current Issues in Tourism (11.8%), and Tourism Management Perspectives (11.2%). The remaining six journals featured a relatively smaller number of articles as presented in Table 2. Notably, the cumulative count of articles from the top three journals amounted to 56.2%, indicating their significant emphasis on destination image study and considerable attention to advancements within this field. Hence, researchers with a particular interest in destination image study are recommended to prioritize their focus on these three aforementioned journals.

Table 2. Publications by Journal

Selected Journals	Number of Articles	Percentage
Tourism Management	38	21.3%
Journal of Travel & Tourism Marketing	34	19.1%
Journal of Travel Research	28	15.7%
Current Issues in Tourism	21	11.8%
Tourism Management Perspectives	20	11.2%
Journal of Hospitality & Tourism Research	13	7.3%
Annals of Tourism Research	8	4.5%
Journal of Sustainable Tourism	7	3.9%
Tourism Geographies	4	2.2%
International Journal of Contemporary Hospitality Management	4	2.2%
International Journal of Hospitality Management	1	0.6%

Within the domain of destination image research, there exist noteworthy authors who have made substantial contributions in this field. Table 3 provides information about scholars who have published more than 3 articles between January 2012 and April 2023. It is important to note that the statistics presented in this table do not consider the authors’ ranking among all contributors of the respective articles. Leading the list is Dimitrios Stylidis from Middlesex University in the United Kingdom, with 11 articles to their name, followed by Xiang (Robert) Li from Temple University with 7 articles. Both Kyle Maurice Woosnam from the University of Georgia and Svetlana Stepchenkova from the University of Florida in the United States have authored five articles each. William Cannon Hunter from Kyung Hee University has published 4 articles, while an additional nine authors have contributed 3 articles each. Tracking the research outcomes of these authors facilitates convenient access to the advancements made in destination image research, specifically concerning the research topics they have focused on.

Table 3. Productive authors

Productive Authors	Affiliations	Number of Articles
Dimitrios Stylidis	Middlesex University, UK	11
Xiang (Robert) Li	Temple University, USA	7
Kyle Maurice Woosnam	University of Georgia, USA	5
Svetlana Stepchenkova	University of Florida, USA	5
William Cannon Hunter	Kyung Hee University, South Korea	4
Amir Shani	Ben-Gurion University of the Negev, Israel	3
Chun-Chu Chen	University of Idaho, USA	3
Choong-Ki Lee	Kyung Hee University, South Korea	3
Eli Avraham	University of Haifa, Israel	3
Kyriaki Kaplanidou	University of Florida, USA	3
Kun Lai	Sun Yat-sen University, China	3
Vincent Wing Sun Tung	The Hong Kong Polytechnic University, China	3
Yaniv Belhassen	Ben-Gurion University of the Negev, Israel	3
Asli D.A. Tasci	University of Central Florida, USA	3

4.3. Research topics

The realm of destination image research comprises three primary topics and thirteen subtopics, as visually depicted in Figure 5, illustrating the interconnections between these research topics. The quantity and proportion of articles pertaining to the primary topics and subtopics are presented in Table 4. Roman numerals (I, II, and III) are employed to denote the three primary topics, while lowercase letters (a to j) in the English alphabet are utilized to represent the subtopics. Each subtopic signifies a distinct research domain within the overarching field of destination image. Notably, the designations f-j, g-j, and i-j indicate studies that span across two distinct research fields.

Figure 5. Research topics of destination image.

Table 4. Distribution of research topics

Primary Topics	Content	Articles (Percentage)	Subtopics	Content	Articles (Percentage)
I	Destination image ontology research	53 (29.8%)	a	Projected image research	8 (4.5%)
			b	E-WOM image research	17 (9.6%)
			c	Perceived image research	28 (15.7%)
II	Interplay of projected image, e-WOM image, and perceived image	26 (14.6%)	d	Comparison of projected image and e-WOM image	10 (5.6%)
			e	Comparison of projected image and perceived image	3 (1.7%)
			f	Impact of projected image on perceived image	1 (0.6%)
			f-j	Impact of projected image on tourists through perceived image	6 (3.4%)
			g	Impact of e-WOM image on perceived image	1 (0.6%)
			g-j	Impact of e-WOM image on tourists through perceived image	4 (2.2%)
			h	Comparison of e-WOM image and perceived image	1 (0.6%)
III	Impact of other factors on perceived image and interplay with travel behaviors	99 (55.6%)	i	Impact of other factors on the perceived image	24 (13.5%)
			i-j	Impact of other factor on tourists through perceived image	27 (15.2%)
			j	Perceived image and travel behavior interaction	48 (27%)
Total		178 (100%)			178 (100%)

Prior to analyze the subtopics presented by Primary topic I in Table 4, it is crucial to clarify the term “e-WOM image”, which refers to destination image conveyed through UGC on electronic platforms like social media and e-commerce websites. Although there has been research on e-WOM, the specific concept of e-WOM image has not been explicitly addressed in the existing literature. The e-WOM image is derived from various forms of information shared by tourists, including reviews, photos, videos, and ratings, that pertain to a destination. They serve as a reflection of the tourists’ experiences and perceptions of the destination, encompassing both the period during and after their visit.

Numerous scholars employ e-WOM image as a proxy for perceived image (González-Rodríguez et al., 2016; Marine-Roig & Ferrer-Rosell, 2018; Sun et al., 2014), however, it is essential to differentiate between the two concepts, as they exhibit distinct characteristics. The e-WOM image relies on UGC as its data source, which possesses certain limitations in capturing the comprehensive perceived image. These limitations include its reliance on post-trip sharing behavior, which overlooks pre-trip perceptions, and its dependence on socially active users, thereby neglecting diverse tourist groups such as non-social media users or those who refrain from sharing. Therefore, the e-WOM image does not provide a whole representation of the perceived image.

In addition, the e-WOM image has the potential to influence the perception of a destination among prospective tourists. Extensive research has been conducted to examine the impact of e-WOM on the perceived image (Purbadharmaja et al., 2021; Yerizal & Abror, 2019). From the tourists’ perspective, they are influenced by the e-WOM image when receiving information, as well as actively contribute to the creation of e-WOM image when sharing information (Camprubí et al., 2013; J.-S. Lee & Park, 2023). As a result, the e-WOM image not only influences the perceived image but also forms an integral part of it. By distinguishing the e-WOM image from the perceived image, researchers can acquire a more profound comprehension of the interconnections and associations among various topics within the realm of destination image research.

Primary topic I in Table 4 pertains to the ontology of destination image research, which includes its definition, composition, analysis of characteristics, and measurement of attributes. Within this domain, three subtopics are identified: subtopic a focuses on research concerning projected image, specifically examining contents generated by DMOs; subtopic b investigates UGC related to a destination, referred to as e-WOM image in this study; and subtopic c explores the perceived image of tourists or non-tourists (e.g., potential travellers).

Primary topic II in Table 4 delves into research on the interplay among the projected image, e-WOM image, and perceived image, as well as the impact of the former two on travel behavior through the perceived image. This topic comprises seven subtopics: subtopic d explores the comparison between the projected image and e-WOM image, subtopic e examines the comparison between the projected image and perceived image, subtopic f investigates the influence of the projected image on the perceived image, subtopic f-j delves into the influence of the projected image on tourists via the perceived image, subtopic g explores the influence of e-WOM image on the perceived image, subtopic g-j investigates the influence of e-WOM image on tourists through the perceived image, and subtopic h explores the comparison between the e-WOM image and perceived image. The perceived image consistently holds a central position in this research domain and plays a crucial moderating role in the influence of the projected image and e-WOM image on tourists (Assaker & O’Connor, 2020; Gong & Tung, 2017; Y. Li et al., 2023; Nicoletta & Servidio, 2012).

Primary topic III in Table 4 focuses on examining the influence of other factors on the perceived image, in addition to the projected image and e-WOM image, as well as the dynamic interplay between the perceived image and travel behaviors. The additional factors include a range of elements, including country image, politics, culture, media, crisis, sports events, products, beliefs, and more. Within this topic, three subtopics are identified: subtopic i investigates the impact of other factors on the perceived image, subtopic i-j explores how these factors, mediated through the perceived image, affect tourists, and subtopic j examines the reciprocal interaction between the perceived image and travel behaviors.

According to the data presented in Table 4, Primary topic III emerges as the most prominent area of study among the three primary topics, constituting 55.6% of the total. This finding emphasizes the significance of factors influencing the formation of destination image and the dynamic interplay between the perceived image and travel behavior within the field of destination image research. In second place is Primary topic I, accounting for 29.8% of the studies, indicating sustained attention towards the ontology of destination image. Primary topic II, however, represents the smallest share with only 14.6%, highlighting the need for further exploration of the relationship between the projected image, e-WOM image, and perceived image.

Moving on to the subtopics, topic j emerges as the most extensively researched, accounting for 27% of the studies. This underscores the significance of investigating the interaction between the perceived image and travel behavior, representing a crucial research field. Subsequently, research on perceived image (c) and the impact of other factors on perceived image and travel behavior (i-j) account for 15.7% and 15.2% respectively. Research on the influence of other factors on perceived image (i) follows suit, accounting for 13.5%. Conversely, the research findings regarding topics e, f, g, and h are the least extensive, suggesting that these subject areas have not received comprehensive attention and offer potential opportunities for further investigation.

Regarding the time distribution of research topics as shown in Table 5, several distinct trends can be observed. Firstly, research on the projected image itself (a) ceased after 2018, and the comparison between the projected image and the perceived image (e) became non-existent after 2019. Secondly, there has been a significant decline in research on perceived image (c) in recent years, while research on e-WOM image (b) has experienced a relative increase. Thirdly, comparative studies on projected image and e-WOM image (d) have seen a notable rise since 2017, and studies investigating the influence of e-WOM image on tourists through perceived image (g-j) have also exhibited some growth. Lastly, comparative research on e-WOM image and perceived image (h) is set to commence in 2023. In recent years, there has been a noticeable shift towards increased focus on research pertaining to e-WOM image. This trend highlights numerous research opportunities within this domain.

Table 5. Distribution of subtopics by year

Subtopics	Content	Year
		2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
a	Projected image research	2	1	-	1	2	2	-	-	-	-	-	-
b	E-WOM image research	-	-	2	2	2	1	1	2	-	6	1	-
c	Perceived image research	6	3	3	2	4	2	2	2	2	2	-	-
d	Comparison of projected image and e-WOM image	-	2	-	-	-	3	1	-	1	1	1	1
e	Comparison of projected image and perceived image	-	1	-	-	-	1	1	-	-	-	-	-
f	Impact of projected image on perceived image	-	-	-	-	-	-	-	-	1	-	-	-
f-j	Impact of projected image on tourists through perceived image	1	-	1	-	-	1	1	-	-	1	-	1
g	Impact of e-WOM image on perceived image	-	1	-	-	-	-	-	-	-	-	-	-
g-j	Impact of e-WOM image on tourists through perceived image	-	1	-	-	-	-	-	1	1	1	-	-
h	Comparison of e-WOM image and perceived image	-	-	-	-	-	-	-	-	-	-	-	1
i	Impact of other factors on the perceived image	2	3	-	3	4	1	1	1	1	4	2	2
i-j	Impact of other factor on tourists through perceived image	-	3	1	1	5	1	2	4	2	5	3	-
j	Perceived image and travel behavior interaction	1	8	4	2	1	8	3	7	6	4	1	3

Research on the e-WOM image relies on the analysis of UGC. Despite several years of UGC research, its primary usage has been limited to serving as a data source for analyzing perceived image that capture tourists’ sentiments towards a destination during or after their visit (Iglesias-Sanchez et al., 2020; Lalicic et al., 2021; Tseng et al., 2015). Khan et al. (2021) cited the destination formation model proposed by Gunn Clare (1972) and highlighted the significance of UGC as a vital component of organic information that shapes the impressions of potential tourists regarding a destination. By differentiating the e-WOM image from the perceived image and elevating e-WOM image as a key research area, UGC analysis holds tremendous potential for further advancement and development.

4.4. Research methods

In the rapidly evolving field of destination image research, understanding the advancements in research methods is crucial. This section discusses the methodologies employed by the reviewed articles. Qualitative research methods focus on words and meanings, while quantitative research methods emphasize numbers and statistics topic (Brannen, 1992; Harrison et al., 2001). In this study, quantitative methods involved closed-ended questionnaires surveys, experiments, machine learning and big data analyses. On the other hand, qualitative methods encompassed focus groups, observation, interviews, free elicitation, free association procedure, employed photography, and theoretical analysis. Content analysis and meta-analysis could be either quantitative or qualitative, with a majority of them being mixed methods.

Figure 6 illustrates the trends in qualitative, quantitative, and mixed methods used in research between January 2012 and April 2023. Overall, 54.5% of the articles employed quantitative methods, 39.3% used mixed methods, and 6.2% utilized qualitative methods. The number of purely qualitative research articles was limited, with only 11 in total. From 2015 to 2023, quantitative research dominated, reaching 85.7% in 2020. However, there was a subsequent decline, and the trend shifted towards mixed methods. Mixed methods experienced a decline starting in 2015 but saw a resurgence in 2020. A notable observation is that since 2021, the proportion of articles utilizing the three research methods has become more balanced, and the dominance of quantitative research has diminished.

Figure 6. Trends in research methods.

There is an overview of the specific research methods utilized across all reviewed articles as shown in Figure 7. It should be noted that some articles employed multiple research methods. For instance, Dolnicar and Grün (2012) incorporated a questionnaire, interviews, and experiments in their study. In the field of destination image research, the most commonly employed methods are questionnaire, content analysis, interview, and experiment. Among these, the questionnaire method holds the highest prevalence, accounting for over half of the studies at 50.7%. This aligns with the dominance of quantitative research highlighted in Figure 6. The second most utilized method is content analysis, representing 23.1% of the studies, encompassing both DMO-generated content and analysis of UGC. Given that media information significantly influences destination image formation (Farhangi & Alipour, 2021; Sultan et al., 2021), content analysis holds substantial value in investigating the projected image and e-WOM image. With the advancement of big data analysis technology, content analysis has witnessed an increasing utilization of methods such as computer-based data mining and machine learning (He et al., 2021; Xiao et al., 2022). These techniques improve data processing effectiveness, enhance the accuracy and reliability of results, and have potential for wider application in destination image research. Interviews and experiments account for 12.2% and 7.0% respectively, while other research methods such as observation, free elicitation, employed photography, and meta-analysis collectively contribute to 7.9% of the studies. In the field of destination image research, certain prominent research methods have demonstrated notable advantages, particularly questionnaire surveys. However, future investigations could consider exploring a wider range of research methodologies to enhance the advancement of understanding in this domain.

Figure 7. Proportions of specific research methods.

5. Conclusion

This paper presents an updated systematic review of destination image research. By conducting an extensive review of significant literature, this study offers an overview of the research progress made in theoretical models pertaining to the conceptual framework, formation factors, and formation process of destination image. Following the PRISMA process, a rigorous selection was made, including 178 peer-reviewed academic papers published in 11 prominent journals between January 2012 and April 2023 as the research sample. The selected articles underwent analysis using the meta-analysis methodology. The findings of this study reveal the characteristics and trends of destination image studies, while also offering insights into potential avenues for future research.

The volume of publications showed considerable fluctuations without a distinct overall upward or downward trend. Notably, 2021 and 2013 stood out as productive years for destination image research. When conducting destination image studies, particular attention should be given to the top three journals, namely Tourism Management, Journal of Travel & Tourism Marketing, and Journal of Travel Research. Prolific authors are highlighted, and tracking their research facilitates convenient access to the latest advancements in a specific field.

The term “e-WOM image” was introduced during the analysis of the research topic. By differentiating the e-WOM image from the perceived image, the components of destination image research can be presented with greater clarity, enhancing the internal logic of research topics. The entire research field was categorized into three primary topics and thirteen subtopics. Among these, the perceived image occupies a central position, encompassing its measurement, various influencing factors, and the interactive relationship with travel behaviours, thus representing a significant proportion of research topics. In recent years, the focus on projected image and perceived image has decreased, while research on e-WOM image and related areas has experienced a notable increase. This trend has given rise to notable research areas, such as comparative studies between projected image and e-WOM image, as well as investigations into the impact of e-WOM image on tourists through the perceived image. Additionally, there has been a growing interest in comparative analysis between e-WOM image and perceived image. The analysis of UGC plays a vital role in studying the e-WOM image. As the research on e-WOM image garners more attention, there is immense potential for further progress and growth in the analysis of UGC.

The analysis of research methods indicates a consistent dominance of quantitative research. However, starting from 2021, the prevalence of quantitative research has shown signs of weakening, accompanied by an increase in the utilization of qualitative research. Consequently, the proportions of quantitative research, qualitative research, and mixed methods have become more balanced. From the perspective of specific research methods employed, the questionnaire survey and content analysis exhibit notable advantages, with the questionnaire survey being particularly prominent, representing 50.7% of the methods utilized. Big data analysis techniques, including computer-based data mining and machine learning, are increasingly used in content analysis. In the future, it is recommended to explore a wider range of research methods to facilitate a more comprehensive understanding of this field.

6. Contributions and Limitations

Destination image research holds a pivotal position in the field of tourism studies. However, there is a noticeable dearth of review papers exclusively dedicated to this subject. This study aims to bridge this gap by offering a comprehensive analysis of the characteristics and trends found in destination image studies, while also presenting implications for future investigations.

Firstly, the e-WOM image of a tourist destination is a research area with significant potentials. Further clarification is required regarding its conceptual framework, as well as an in-depth examination of its constructs, attributes, and measurement methods. Secondly, the formation process of e-WOM image, the factors of its formation, and its impact on potential tourists warrant further investigation. Variations in media characteristics, cultural backgrounds, and social environments may influence the formation of the e-WOM image, presenting ample opportunities for research in this domain. Thirdly, it is crucial to examine the interrelationships among projected image, e-WOM image, and perceived image. The dynamic mechanisms between these three elements are yet to be fully understood. Comparative studies of e-WOM image and projected image, as well as e-WOM image and perceived image, require further in-depth exploration. Fourthly, with the advancement of big data analysis technology, computer-based data mining and machine learning methods are anticipated to be increasingly employed in the analysis of UGC, specifically when investigating the e-WOM image. Moreover, embracing diversified research methods, such as free association, image matching, and grounded theory surveys, holds the potential to advance knowledge in this field. Conducting research in such fields can significantly enhance the understanding of destination image and provide valuable insights for DMOs to develop effective online marketing strategies for an improved destination’s profile.

The present study acknowledges several limitations. Firstly, the focus of this study was exclusively on literature published in 11 journals within the field of tourism and hospitality research, spanning from January 2012 to April 2023. The exclusion of other journals, conference proceedings and books may introduce potential bias and limit the generalizability of the findings. Secondly, the scope of retrieval in this study was confined to titles, abstracts, and keywords, which may result in an incomplete collection of relevant literature. These limitations can be addressed in future research by expanding the coverage of databases and conducting more extensive literature searches. Furthermore, the analysis of research methods in this study primarily focused on identifying the main methods employed in each article, potentially resulting in a limited depth of analysis. To address this limitation, future research can undertake a more comprehensive examination of the data collection and processing processes. These endeavors would contribute to the advancement of destination image research.

Acknowledgments

The authors gratefully acknowledge the support of the Graduate School and International College of Digital Innovation at Chiang Mai University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Zuo Wang

Zuo Wang is a PhD student in Digital Innovation and Financial Technology at Chiang Mai University, also serving as a lecturer in the School of Tourism and Culture Industry at Chengdu University, specializing in digital media and destination marketing.

Piyachat Udomwong

Dr. Piyachat Udomwong is a lecturer at the International College of Digital Innovation, Chiang Mai University. She earned her Ph.D. in Biology (Bioinformatics) from University of York, UK, with a background in mathematics, thriving in multidisciplinary settings. Her research interests are to discover insights from a massive amount of data coming from different fields by using data analytics, machine learning, and statistical learning techniques.

Jing Fu

Dr. Jing Fu is an associate professor in the School of Tourism and Culture Industry at Chengdu University, specializing in smart tourism and hospitality, as well as knowledge management in the field of tourism and hospitality. She earned her Ph.D. in Knowledge Management from Chiang Mai University in 2012. Subsequently, she was a visiting scholar at the University of Lyon 2, France, and undertook an executive development program at the University of New Hampshire, USA. She conducted post-doc research at the Department of Tourism and Hospitality Management, School of Business Administration, International Hellenic University, Greece, between 2013-2014. During her sabbatical year in 2018-2019, she served as program manager at the Hong Kong Polytechnic University's School of Hotel and Tourism Management.

Pintusorn Onpium

Dr. Pintusorn Onpium, a lecturer at Chiang Mai University's International College of Digital Innovation, conducts research in cultural tourism management, as well as tourism planning and development. She earned her Ph.D. in Integrated Tourism Management from the National Institute of Development Administration, Thailand. She actively collaborates with the local community, fostering their growth by generating revenue and creating jobs through Community-Based Tourism (CBT) and cultural capital initiatives.