Perceptions of Destination Image among Japanese Tourists toward the Hong Kong and Macau Regions of China: An Analysis Based on Online Reviews

Perceptions of Destination Image among Japanese Tourists toward the Hong Kong and Macau Regions of China: An Analysis Based on Online Reviews | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Perceptions of Destination Image among Japanese Tourists toward the Hong Kong and Macau Regions of China: An Analysis Based on Online Reviews Qiyuan Gong, Mengmeng Sun, Wenkai Shi, Yuwei Wang, Yang Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6637197/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Against the backdrop of intensified global tourism competition, the perception of destination image has become increasingly influential in tourist decision-making. However, existing research has lacked in-depth exploration of the image perception mechanisms of Japanese tourists in the Hong Kong and Macau regions, particularly the dynamic analysis based on user-generated content (UGC) remains underexplored. Drawing upon online reviews authored by Japanese tourists between 2009 and 2023, this research applies sentiment analysis, high-frequency lexical analysis, and a core–periphery modeling approach to uncover the defining characteristics of their perceptions and their underlying mechanisms. The results indicate that: (1) Japanese tourists predominantly exhibit positive sentiments toward Hong Kong and Macao, primarily driven by natural scenery, historical and cultural attractions, and transportation infrastructure, yet express negative feedback regarding issues such as attraction facilities and service quality. (2) The evolution of the tourists’ destination image perceptions reflects a diversified trend, shifting from singular cultural sightseeing to composite culture-entertainment experiences, with modernized resources and interactive activities emerging as new satisfaction drivers. (3) Tourism resources and activity experiences constitute the core elements of tourist perception, while service quality, transportation facilities, and urban development occupy peripheral positions that play a supportive role in enhancing tourist satisfaction. (4) The synergistic effect between tourism resources and activity experiences exerts the strongest influence, with peripheral factors—service quality, transport infrastructure, and urban growth—indirectly enhancing the central experiential components. Humanities/Language and linguistics Social science/Sociology Destination Image Perception Japanese Tourists Hong Kong and Macau Regions Core-Periphery structure Model Figures Figure 1 Figure 2 1. Introduction The destination image plays a decisive role in tourism decisions.The concept of "image" refers to the the overall impression that consumers have of a brand or product, encompassing cognition, emotion, and attitude (Levy, 1978) [1] . In contexts of information overload, this image becomes a decisive factor in the decision-making process. Destination image, defined as an individual’s cognition, beliefs, and feelings regarding a specific destination (Crompton, 1979; Fakeye & Crompton, 1991; Baloglu & McClery, 1999b) [2–4] . This concept is applicable both to consumer goods markets and to the tourism industry. Hunt (1971) [5] first introduced the notion of destination image into tourism research, establishing the perception of tourism destination image as a a critical academic focus (Stepchenkova & Mills, 2010; Gallarza et al., 2002) [6–7] . Tourism destinations compete through image-building to attract visitors, with stronger positive impressions correlating to higher likelihoods of visitation. Indeed, destination image is recognized as a key determinant influencing travel decisions (Mayo, 1975; Beerli & Martin, 2004; Chen & Tsai, 2007) [8–10] . Research on destination image has predominantly focused on the interplay among the environment, the destination, and the tourist. Within this framework, image—as a synthesis of subjective values and accumulated knowledge—profoundly influences tourist decision-making and their interaction with the external environment (Boulding, 1956) [11] . By cultivating a superior environment, tourism destinations can enhance their image, thereby attracting more visitors and initiating a virtuous cycle from environmental improvement to increased tourist attraction. By developing a favorable environment, tourism destinations can enhance their image and, in turn, attract more visitors, creating a virtuous cycle from environmental improvement to tourist attraction. When selecting a destination, tourists rely on the destination image to assess factors such as the environmental conditions, safety, service quality, and peer evaluations. In light of evolving tourist preferences and intensifying competition among emerging destinations, the importance of total quality management in the tourism industry has become increasingly evident (Camison, 1996) [12] . Moreover, consumer experience has been found to be more compelling than the intrinsic features of products or services (Pine & Gilmore, 1999) [13] . Consequently, destination managers must maintain high-quality facilities, human resources, and other consumer-focused services to provide a pleasant customer experience (Ali et al., 2018) [14] . This strategy not only addresses tourists’ heightened expectations regarding environmental and service quality but also encourages continuous improvements, establishing a positive feedback loop among the environment, the destination, and the tourists. These theoretical frameworks provide a critical foundation for subsequent research on destination image perception. The research on destination image perception has achieved multi-dimensional paradigm evolution.Destination Image Perception is defined as the aggregate of an individual’s beliefs, thoughts, and impressions regarding a particular destination. In the context of tourism, destination image perception reveals the degree to which tourists value and evaluate destination attributes—such as natural scenery, cultural heritage, and service quality. Over half a century of theoretical refinement and methodological innovation, research on destination image perception has exhibited a distinct paradigm shift. Initial studies focused on cognitive-affective composite dimensions, with Crompton’s (1979) [15] dual-component model (cognitive and affective) and Gartner’s (1993) [16] three-stage formation mechanism serving as the core, establishing a foundational measurement system based on questionnaires and factor analysis (Pizam & Sussmann, 1995) [17] . With the increasing complexity of tourism systems amid globalization, the research perspective expanded in the early 21st century to encompass operational management practices, resulting in the development of four core branches:(1) Crisis-Driven image restoration mechanisms (Avraham, 2016; Ritchie & Jiang, 2019) [18-19] ; (2) Eco-Community synergy models oriented toward sustainable development (Line & Hanks, 2016; Budeanu et al., 2016) [20-21] ; (3) dynamic evaluation frameworks for destination brand equity (Pike & Page, 2014) [22] ; and (4) the moderating effects of cultural identity on tourist decision-making (Ozdemir & Yolal, 2016; Lee et al., 2019b) [23-24] . Furthermore, with the rapid advancement of digital technology, research has gradually shifted from traditional qualitative analytical methods to data-driven approaches, such as the utilization of big data and social media (Wang et al., 2019; Park et al., 2019; Ren & Hong, 2017) [25-27] and user-generated content (UGC) (Marine-Roig, 2017; Zhang et al., 2021;Yamagishi K et al.,2024) [28-30] . The digital technology revolution has reshaped the methodology of tourism image research.Online user-generated content (UGC) platforms have become increasingly vital for destination management and promotion (Kirilenko et al., 2019) [31] . UGC not only shapes the online destination imagery but also influences tourists' perceptions during their browsing process, thereby motivating their travel decisions and destination choices (Wong & Qi, 2017) [32] . The continuous advancement of technology has placed destination marketing organizations (DMOs) in a highly competitive environment, prompting them to manage and innovate their services to enhance their online destination imagery (Jimenez-Barreto et al., 2019) [33] . The advancement of technology enables tourists to create and share their impressions and reviews of destinations online (Lian & Yu, 2017; Mak, 2017) [34-35] . Travelers' first impressions and decisions increasingly rely on reviews from others who have visited the destination (Lam et al., 2020) [36] . Consequently, online digital information has emerged as a key reference for destination managers in shaping destination image (Choi et al., 2007; Xiang, 2010) [37-38] , while also significantly influencing tourists' decisions and behaviors (Baloglu & Brinberg, 1997; Wang & Hsu, 2010) [39-40] . Social network analysis has become a key paradigm for analyzing complex tourism systems.The theoretical trajectory of SNA in tourism research has undergone three distinct phases: Early studies focused on describing the structures of tourism collaboration networks(Selin & Chavez, 1995), relying on small-sample static analyses. With the widespread adoption of tools like UCINET, research extended to various contexts, including spatial patterns of tourism flows(McKercher et al., 2012) [42] and stakeholder power networks (Dredge, 2006) [43] . In recent years, SNA has deeply integrated with big data and artificial intelligence technologies, leading to methodological advancements such as dynamic network modeling (Li et al., 2023) [44] and multimodal data fusion( Park et al., 2019) [45] . These advancements have unveiled deeper mechanisms, like crisis propagation pathways (Ritchie & Jiang, 2019) [46] and social media sentiment diffusion (Li et al., 2017) [47] . Current research has established theoretical frameworks, such as network indicator evaluation systems (e.g., centrality-vulnerability correlation models). However, challenges persist, including the lack of longitudinal data, insufficient sensitivity to cultural contexts, and debates over data ethics. Japan serves as one of the most significant source markets for Hong Kong and Macau, characterized by strong purchasing power and substantial contributions to their tourism-driven economies.Additionally, the cultural connections and distinctions between Japan and China provide a unique context for understanding Japanese tourists' perceptions of destination image in Hong Kong and Macao. Investigating their perceptions aids in comprehending the needs and preferences of this demographic, while offering cross-cultural comparative value for studies on other international markets. However, existing research predominantly focus on European and American source markets, lacking targeted exploration of Japanese tourists' dynamic perceptions in densely populated urban destinations like Hong Kong and Macao. A notable theoretical gap persists in understanding the synergistic mechanisms between cultural symbolism and technological infrastructure in shaping destination perceptions. Furthermore, the application of social network analysis (SNA) in tourism destination image perception research remains nascent, presenting substantial underexplored potential for analyzing the dynamic changes and influencing mechanisms of specific destination image perceptions. This study delves into the perceptual characteristics and temporal dynamics of Japanese tourists’ destination image perceptions of China’s Hong Kong and Macau, aiming to identify key influencing factors and their mechanisms to enhance these regions' competitiveness in the international tourism market. Employing sentiment analysis, high-frequency word analysis, co-occurrence network analysis, and a core-periphery model, the research comprehensively analyzes Japanese tourists' destination image perceptions from multiple perspectives, revealing dynamic changes in these perceptions, and identifying key influencing factors and interaction mechanisms. The findings not only provide theoretical support and practical guidance for the management and optimization of tourism destinations in Hong Kong and Macau,but also establishes a reference paradigm for studying perceptions of Chinese tourism destinations among international tourists. The main contributions of this research can be summarized as follows:(1)It reveals the perceptual characteristics of Japanese tourists toward the tourism image of Hong Kong and Macau across different time periods, deepening the understanding of the evolution of tourist perceptions over time and providing strong data support for long-term planning and strategic adjustments of tourism destinations.(2)The integrated use of diverse analytical methods enhances the depth of the research. Initially, a co-occurrence network is constructed for semantic association analysis, followed by the application of social network analysis methods to uncover interaction characteristics among review subjects, and finally, a core-periphery structure model is employed to identify key influencing factors within the overall network. This approach facilitates a comprehensive and in-depth analysis of Japanese tourists' review data. (3)The research identifies the key factors and mechanisms influencing Japanese tourists' perceptions of the tourism image of Hong Kong and Macau. The structure of this paper is as follows: Section 1 is the introduction; Section 2 introduces the overview of the research area and research methods; Section 3 explores Japanese tourists' perceptions of the tourism image of Hong Kong and Macau through text analysis; Section 4 delves into the research results, advantages and limitations, and proposes corresponding policy recommendations; Section 5 is the conclusion. 2. Methodology 2.1 Study site As observed in Figure 1, Hong Kong and Macau, as China's two Special Administrative Regions (SARs), are situated in the Pearl River Delta, forming a unique cross-border tourism city cluster. Governed under the “One Country, Two Systems” framework, both regions exhibit hybrid governance models blending post-colonial legacies with contemporary Chinese urbanism. Covering a combined area of 3106 km² and accommodating a population of 7.8 million, Hong Kong and Macau represent quintessential examples of ultra-high-density urban environments, with population densities of 6,801 and 21,340 persons per square kilometer, respectively. These characteristics render them ideal case studies for examining tourism experiences within spatially constrained settings. The Hong Kong Special Administrative Region, located east of the Pearl River estuary, boasts a comprehensive transportation network, rich historical and cultural heritage, and numerous tourist attractions, earning it the “shopping paradise,”that harmonizes Eastern and Western cultural influences. Macau Special Administrative Region, situated on the western side of the Pearl River estuary, is renowned for its gaming industry, abundant historical sites, and modern entertainment facilities, creating a distinctive cultural identity. Annually, both regions receive an average of 65.8 million visitors (rebounded to 58% of pre-pandemic levels in 2022), with Japanese tourists accounting for 8.7% of international arrivals. 2.2 Data Sources This study utilizes online reviews and video data published by Japanese tourists in Hong Kong SAR and Macau SAR, China, from 2009 to 2023. Textual data was sourced from the followingplatforms:4travel:https://4travel.jp/os_area_country-china.html;Tripadvisor:https://www.tripadvisor.jp.Video data is extracted from YouTube:https://www.youtube.com. A hybrid approach combining automated algorithms and manual verification was applied to clean and filter the raw data, including the removal of duplicate entries, irrelevant or non-substantive reviews, and content unrelated to the research focus. Ultimately, 10,778 valid reviews are retained for analysis. 2.3Period division The research collected and analyzed the online comments posted by Japanese tourists in Hong Kong and Macao, China from 2009 to 2023. Considering the policy cycle, key events and the evolving trends of tourists' perceptions comprehensively, the period from 2009 to 2023 was divided into three stages: The periods from 2009 to 2013 (the period of focus on cultural heritage), 2014 to 2018 (the period of transformation towards modernization and entertainment), and 2019 to 2023 (the period of crisis response and technological empowerment) are hereinafter referred to as the early, middle and late stages respectively. 2.4 Methods In this research, the sentiment analysis module of the ROST CM software is calibrated with a localized Japanese lexicon to ensure accuracy in cross-language analysis, ensuring consistency in classification. The ROST CM software is employed to perform sentiment analysis on the collected online review texts, categorizing the comments into three sentiment levels: positive, neutral, and negative [48] . To refine granularity, positive and negative sentiments are further subcategorized, enabling precise identification of tourists’ affective orientations toward various aspects of Hong Kong and Macau. The corpus analysis tool KH Coder is an open-source software designed for quantitative text analysis and text mining. As a semantic analysis tool, it offers comprehensive functionality, including frequent updates, and supports simplified management. Notably, KH Coder provides visual mapping capabilities that intuitively elucidate intrinsic associations between high-frequency terms and themes [49] . In this research, KH Coder is utilized for data processing of the self-constructed corpus, employing high-frequency word analysis and co-occurrence network analysis. High-frequency word analysis identifies prominent themes and focal points in textual data by statistically extracting the most recurrent terms. In this research, KH Coder software is employed to conduct frequency statistics on the collected online reviews, extract high-frequency vocabulary, and categorize them based on perceptual dimensions and sentiment analysis results. Co-occurrence network analysis is a method used to uncover the relationships between words by examining their simultaneous appearances within a given context. By constructing a co-occurrence matrix of high-frequency terms and visualizing them as interactive network graphs, the analysis quantified the strength of associations between different terms, revealing the interconnections among various perceptual elements mentioned in tourists’ reviews. The core-periphery model, a method within social network analysis (SNA) [50] , is designed to identify core and peripheral elements within a network. This model is particularly suitable for analyzing hierarchical structures, effectively distinguishing between dominant factors (core) and auxiliary factors (periphery) within the high-density resources of Hong Kong and Macau. In this research, Ucinet is utilized to conduct core-periphery analysis on the co-occurrence network, aiming to identify the core and peripheral elements influencing tourist perceptions across different time periods. 3. Results 3.1 Sentiment Analysis The sentiment analysis conducted on the textual data categorized emotions into three levels: positive, neutral, and negative, with further subdivisions within positive and negative sentiments, as detailed in Table 1.Japanese tourists' perceptions of Hong Kong and Macau predominantly exhibited positive sentiments, comprising 91.39% of the total comments, with 36.49% reflecting highly positive sentiments. Neutral and negative sentiments represented 2.17% and 6.44%, respectively, indicating a strong reputation of these regions among Japanese tourists, with the vast majority having positive experiences. Analysis of the content within positive sentiments revealed appreciation for aspects such as natural landscapes ("abundant natural scenery viewed from surrounding skyscrapers"), cultural experiences("locations steeped in historical ambiance"), shopping and entertainment("diverse shopping malls and entertainment options"), service quality and management ("comprehensive and attentive service across all roles"), transportation efficiency("free shuttle buses from the airport; highly convenient transit"), and urban development ("ongoing infrastructure projects and dynamic city growth"). Conversely, negative sentiments primarily focused on issues related to attraction facilities and experiences ("although crowded, it's just for photos; it feels somewhat desolate, and many amusement facilities are closed"), travel transportation and accessibility ("Macau Tower’s poor connectivity; hard to reach on foot from major tourist spots"), service quality and management (" uneven hotel service quality; some staff have indifferent attitudes"), cultural and environmental adaptation (" Macau's historical sites like the Ruins of St. Paul’s require time to navigate cultural differences"), language barriers ("language barriers in Macau's casinos pose some difficulties"), and tourism planning and information gaps ("lack of information may lead to missing key attractions or activities"). Table 1. Sentiment Analysis Results Emotion Count Percentage Positive emotions 9844 91.39% Neutral emotions 235 2.17% Negative emotions 699 6.44% Breakdown of Positive Emotions: Low(0-10) 2697 25.04% Moderate(10-20) 3216 29.86% High(20 and above) 3931 36.49% Breakdown of Negative Emotions: Low(-10-0) 596 5.53% Moderate(-20--10) 98 0.91% High(-20 and below) 5 0.05% 3.2 High-Frequency Word Analysis As shown in Table 2, this research utilized the word frequency analysis module of KH Coder software to examine high-frequency terms across three distinct periods: 2009–2013, 2014–2018, and 2019–2023. These terms are systematically categorized based on perceptual elements and sentiment analysis results into six primary categories: —tourism resources, activity experiences, service provision, transportation accessibility, infrastructure development level, and others—based on perceptual elements and sentiment analysis outcomes. Table 2 reflects the focus of Japanese tourists on tourism resources in Hong Kong and Macau has undergone significant changes over time. In the early period (2009–2013), high-frequency words included "Macau," "plazas," "churches,""architectural heritage," and "casinos," reflecting a strong interest in Macau's historical culture and religious architecture, as well as its unique architectural style and gambling culture. During the mid-period (2014–2018), the focus shifted to terms like "Hong Kong," "Macau," "travel," "plazas," "architecture," "night view," and "parks," indicating a growing awareness of Hong Kong, a deeper overall recognition of tourism resources in both regions, and a growing interest in entertainment and leisure resources such as night views and parks. In the later period (2019–2023), terms like "Hong Kong," "Macau,""travel," "plazas," and "architecture," remained central. With the emergence of words like "landmarks"" and "Disneyland.", it shows tourists' expectations for the diversity of tourism resources.At the same time, the emergence of attractions and entertainment facilities in Hong Kong and Macao highlights the brand influence of Hong Kong Disneyland, and the absence of specific attraction names in Macau indicates that further efforts are needed in destination branding. As presented in Table 2, the focus of Japanese tourists on activity experiences has shifted from passive sightseeing to immersive and diversified engagement. In the early period (2009–2013), high-frequency words such as "travel," "atmosphere," "photography," and "impressions" indicated that tourists prioritized the overall ambient experiences of their trips and valued commemorative photography at landmarks. During the mid-period (2014–2018), the emergence of terms like "impressions," "amusement facilities," and "performance" signified an increasing interest in experiential tourism. This shift suggests that tourists gradually moved from superficial sightseeing toward more immersive travel experiences. In the later period (2019–2023), while terms like "atmosphere," "photography," "amusement facilities," and "performance" remained prominent, new keywords such as "movie-themed events," "concert tours," "hiking," and "bars" emerged. Highlights growing demand for diversification and personalization of tourist activities, along with an increasing interest in cultural and entertainment experiences. Beyond traditional shopping and sightseeing, Japanese tourists in Hong Kong engaged in movie-themed activities, attended concert touring performances, while in Macau, they explored historic districts through hiking or immersed in the nightlife in bars. These trends highlight a sustained rise in demand for cultural and entertainment-related tourism activities. Based on the results shown in Table 2, Japanese tourists' focus on service quality has gradually expanded from basic accommodation services to dining services and other value-added offerings. In the early period (2009–2013), high-frequency words such as "hotels" and "complimentary services" indicate that tourists primarily emphasized accommodation services. During the mid-period (2014–2018), the emergence of additional terms like "hotels," "complimentary services," "slightly," and "restaurants" suggests a growing concern for local service quality. Compared to the early period, when the focus was mainly on basic accommodation, there was an increasing interest in the dining industry. In the later period (2019–2023), the inclusion of the keyword "gifts" reflects an elevated expectation for high-quality service. Tourists not only valued basic accommodation and dining services but also placed greater emphasis on supplementary services offered by hotels and restaurants, such as "Welcome gifts," "customized services,"This shift highlights an increasing awareness of service details and rising expectations, leading to more refined and comprehensive evaluations of overall service quality. Table 2 provides evidence that Japanese tourists' focus on transportation infrastructure has gradually expanded from basic public transportation options to concerns about efficiency and accessibility. In the early period (2009–2013), high-frequency words such as "buses" and "taxis" reflect that tourists primarily relied on buses and taxis as their main modes of transportation. During the mid-period (2014–2018), the emergence of additional terms like "buses," "ferries," "rush hours," and "escalators" suggests an increase in transportation options, including ferries alongside buses and taxis, thereby enriching tourists' travel experiences. However, the growing variety of transportation modes also led to an increase in congestion issues, reflecting a rising concern about traffic conditions.In the later period (2019–2023), terms such as "buses," "ferries," "rush hours," and "escalators" remained prominent, but the frequent occurrence of "entry" indicates that tourists’ concerns extended beyond the diversity and coverage of transportation modes. They began to place greater emphasis on operational efficiency and convenience, including the seamless of transfers at transport hubs, traffic management at scenic area entrances, and measures for handling peak-hour congestion. This shift underscores tourists' increasing expectations for a seamless and efficient transportation experience, highlighting the importance of connectivity and fluidity in travel. The results in Table 2 suggest that Japanese tourists' perceptions of infrastructure in Hong Kong and Macau exhibit clear, stage-specific changes. In the early period (2009–2013), tourists primarily focused on the architectural details and spatial layouts, with high-frequency words such as “staircases,” “spacious,” and “interior” reflecting their interest in iconic structures like Victoria Peak in Hong Kong and the Ruins of St. Paul’s in Macau. At this stage, their perceptions were largely derived from experiences at single attractions. During the mid-period (2014–2018), the high-frequency terms shifted to include words like “staircases,” “centers,” “pickup points,” and “spacious,”indicating an increased attention to urban centers and transportation nodes. For example, bustling commercial hubs such as Causeway Bay and Tsim Sha Tsui in Hong Kong, along with large integrated resorts like The Venetian Macao in Macau, made transportation convenience and commercial facilities critical components of the tourist experience. At this juncture, tourists’ perceptions began to expand from individual buildings to comprehensive experiences within specific urban areas. In the later period (2019–2023), high-frequency words such as “many,” “skyscrapers,” “area,” “centers,” and “facilities” remained prominent, reflecting a broad-based focus on modern urban development in Hong Kong and Macau. Tourists showed a strong interest in mega infrastructure projects like the Hong Kong–Zhuhai–Macao Bridge, as well as in modern shopping centers and luxury hotels, with heightened expectations for the overall environment and urban landscape of these destinations. At this stage, perceptions evolved from being centered on individual structures to encompassing entire regional complexes, signifying an upgraded holistic evaluation of urban aesthetics. Assessments of urban development became more detailed and integrated, with an expanded dimensionality of perception. This transition from a micro- to a macro-level focus underscores the continuously rising expectations of Japanese tourists regarding their overall tourism experience in the region. As seen in Table 2, Japanese tourists’ perceptions regarding other dimensions indicate a continuously rising overall satisfaction with tourism in Hong Kong and Macau. In the early period(2009–2013), high-frequency words such as “world,” “good,” and “many” reflected a generally positive overall impression of the destination. During the mid-period(2014–2018), the high-frequency words shifted to “good,” “world,” “Japan,” and “delightful,” signaling a marked improvement in tourists’ pleasurable experiences and an enhanced recognition of the convenience of tourism services. In the later period(2019–2023), while terms like “world,” “good,” “Japan,” and “delightful” remained prominent, the frequent appearance of words such as “convenient” further indicates that advancements in transportation and Socioeconomic development have provided tourists with superior travel experiences and more accessible transit conditions. Table 2 High-Frequency Word Categories Perceptual Dimensions High-Frequency Words (2009–2013) High-Frequency Words (2014–2018) High-Frequency Words (2019–2023) Tourism Resources Macau (1273) Plazas (887) Churches (558) Architectural Heritage (506) Casinos (496) Heritage Sites (437) Locations (286) Portuguese (274) The Catholic Church (219) Beautiful (218) Aesthetic Appeal (143) Museums (136) Towers (134) History (103) European Style (102) Attractions (99) Appearance (97) Spacious (96) Parks (91) Hong Kong (90) Beautiful (86) Local Culture (84) Architecture (79) Cathedrals (68) Scenic Views (67) Cultural Elements (68) Hong Kong (3936) Macau (1908) Tourism (1784) Plazas (1556) Architecture (1207) Locations (1170) Churches (878) Beautiful (868) Nightscapes (786) Parks (762) Casinos (758) Skyscrapers (718) Heritage Sites (704) Attractions (509) History (488) Scenic Views (484) St. Paul’s Ruins (477) Disneyland (460) The Catholic Church (460) Victoria Harbour (453) Famous Sites (440) Portuguese (395) Aesthetic Appeal (373) Museums (350) Towers (285) Hong Kong (1357) Macau (669) Tourism (557) Plazas (445) Architectural Clusters (415) Landmarks (342) Heritage Sites (314) Parks (255) Churches (242) Nightscapes (210) Attractions (191) Scenic Views (188) Beautiful (182) Famous (177) Casinos (160) History (158) Disneyland (155) The Catholic Church (142) Aesthetic Appeal (102) Peninsula Areas (93) Travel Experiences (91) Temples (81) Skyscrapers (80) Museums (79) Activity Experiences Tourism (453) Ambiance (254) Photography (211) Impressions (176) Visits (104) Exhibitions (100) Filming (95) Positive Feedback (76) Tour Concerts (74) Bungee Jumping (72) Future Prospects (70) Impressions (799) Ambiance (766) Photography (765) Amusement Facilities (573) Performances (560) Exhibitions (493) Visit (403) Celebrity (379) Expectations (354) Souvenirs (330) Filming (338) Shortcomings (328) Tour Concerts (274) Ambiance (766) Photography (765) Amusement Facilities (573) Impressions (170) Visit (150) Exhibitions (132) Performances (129) Celebrity (128) Expectations (115) Popularity (102) Filming (99) Walking Tours (89) Film-Themed Experiences (84) Tour Concerts (77) Hiking (79) Bars (78) Service Quality Hotels (193) Free (139) Cream (78) Restaurants (70) Hotels (550) Free (564) Slightly (563) Restaurant (387) Slightly (205) Free (163) Hotels (168) Restaurants (114) Souvenirs (92) Transportation Facilities Buses (141) Taxis (113) Buses (960) Ferries (492) Peak Hour Congestion (377) Escalators (320) Buses (284) Ferries (149) Peak Hour Congestion (145) Escalators (128) Entrance(83) Urban Infrastructure Stairs (153) Spacious (96) Interior (96) Centers (94) Flagging (87) Fountains (84) Surroundings (72) Staleness (72) Impressions (93) Stairs (421) Centers (316) Pick-up Points (302) Broadness (321) Districts (368) Middle (289) Many (427) Skyscrapers (200) Districts (196) Centers (128) Stairs (106) Broadness (95) Middle (86) Facilities (82) Local (75) Impressions (93) Miscellaneous Miscellaneous Global (463) Good (290) Many (255) China (214) High (124) Famous (109) Slightly (97) Worth (86) Japan (83) Single (72) Positive (76) Good (1043) Global (878) Japan (669) China (536) High (510) Joyful (444) Utilize (394) Chaos (341) Tokyo (276) Global (372) Good (247) Japan (187) Joyful (178) China (150) High (149) Portugal (88) Duration (85) Utilize (105) Tokyo (74) Convenience (74) 3.3 Core-Periphery Model To analyze the intrinsic connections between high-frequency words, subsequent analysis is conducted employing Social Network Analysis (SNA). The specific procedure involved constructing a co-occurrence matrix of high-frequency words using KH Coder, assigning values to the matrix, converting it into a binary matrix, and subsequently conducting core-periphery analysis via Ucinet software. Results revealed Japanese tourists' perceptions of the destination image of Hong Kong and Macau evolved across different time periods, with distinct core and peripheral elements. In the first phase, the core element was tourism resources, while peripheral elements included activity experiences, service quality, transportation facilities, and urban development level. In the second and third phases, tourism resources and activity experiences constituted the core elements, whereas service quality, transportation facilities, and urban development remained as peripheral elements. Tourism resources and activity experience serve as the primary drivers influencing tourists' perceptions. Tourism resources maintained core status across all phases,valued not only as tangible entities but also for their symbolic cultural significance. For instance, tourism resources such as the European-style architectural clusters in Macau’s historic districts (e.g., the Ruins of St. Paul’s), Hong Kong’s neon signage culture (e.g., Chungking Mansions), and festival rituals (e.g., the Macau International Fireworks Festival) utilize immersive scene design and symbolic encoding to transform natural landscapes, historical relics, and modern urban sceneries into perceptible cultural images, thereby acting as vessels of emotional memory for tourists. In the early stage, tourists’ perceptions predominantly focused on the tangible presentation of these resources, resulting in a unidirectional, sightseeing-oriented experience. Over time, however, tourism resources underwent multidimensional narrative reconstruction, gradually evolving into media that trigger emotional responses and interact deeply with activity experiences. The core status of tourism resources is perpetuated not only by their enduring attractiveness but also by their irreplaceability as the cultural nucleus of the destination. In the second and third phases, activity experience gradually ascended from a peripheral element to a core driving force, with its importance markedly increasing in line with evolving tourist demands. Through mechanisms such as festival rituals and thematic tours, activity experience dynamically activates static tourism resources, effecting a transformation from mere “physical presence” to “emotional memory.” During participation, tourists not only achieve sensory satisfaction but also internalize their experiences through individual narrative frameworks, fostering cultural resonance and identity affirmation. The emergence of activity experience signifies a shift in tourists’ cognitive paradigms—from traditional “scenic viewing” to immersive “participatory experiences”—thereby serving as the central link connecting tourism resources with tourists’ emotions. Its diversity and interactivity inject new vitality into the overall destination image. While persistently occupying a peripheral position service quality plays a crucial supporting role as the guarantor of a smooth and comfortable experience. Enhancements in standardization and professionalism indirectly elevate the perceived value of tourism resources among tourists. Its weak linkage with activity experience suggests that service quality primarily as a threshold constraint on overall experience quality rather than as a dominant factor. In high-density urban environments, the efficiency and personalization of service quality directly influence tourist satisfaction, forming a vital component of the resilient foundation of destination image. As peripheral elements, transportation facilities play a core role in optimizing tourists' mobility experiences and resource accessibility. Through the space-time compression effect, these facilities shortening physical distances between tourists and tourism resources, thereby enhancing the continuity and efficiency of the travel experience. The strong association between transportation facilities and tourism resources reflects a symbiotic relationship: optimizing the transportation network boosts the utilization of tourism resources, while the resources agglomeration, in turn, drives upgrades in transportation infrastructure. In high-density urban environments, the topological structure of transportation facilities directly impacts tourists' mobility, constituting an essential component of the overall support system. Urban infrastructure, as the foundational environment, has consistently been a peripheral element; however, its importance has gradually become more pronounced as tourists' demands for environmental quality increase. By integrating modern construction with historical characteristics, urban infrastructure shapes the unique identity of the destination. Its functional adaptability ensures the accessibility of tourism resources and the integrity of the overall experience, making it a vital component of the resilient foundation of the destination's image. In high-density urban environments, the incorporation of intelligent and miniaturized designs further optimizes the overall tourist experience. Through the integration of technology and services, an innovative pathway—characterized by the "softening of hard infrastructure"—has been established. Each influencing factor within the destination image cognition system exhibits a hierarchical functional differentiation. Tourism resources and activity experiences form the core driving layer, shaping deep tourist cognition through the dynamic activation of cultural symbols and the transformation of emotional memories. In contrast, service quality, transportation facilities, and urban infrastructure comprise the peripheral system, sustaining the overall fluidity and stability of the experience through functional adaptation and support. This hierarchical structure reflects the transformation of tourist cognition from static observation to dynamic participation, and from unidirectional projection to multidimensional interaction. The synergistic interplay among these elements across different levels collectively constructs a comprehensive cognitive network of the destination image. 3.4 Interaction Mechanisms As illustrated in Figure 3, this research qualified the interaction relationships and frequencies among the five influencing factors through a three-stage co-occurrence network analysis. The key findings as follows: Tourism Resources – Activity Experiences (12), Tourism Resources – Urban Infrastructure (12), Tourism Resources – Transportation Facilities (8), Tourism Resources – Service Quality (6), Service Quality – Activity Experiences (3), Service Quality – Transportation Facilities (3), Urban Infrastructure – Activity Experiences (2), Urban Infrastructure – Service Quality (1), and Transportation Facilities – Activity Experiences (1). From Figure 3, observing that the synergy between tourism resources, activity experiences, and infrastructure constitutes the core driving force of destination competitiveness. As the material carrier of destination image, tourism resources undergo immersive scene design and symbolic encoding, engaging in multi-layered interactions with activity experiences. Historical architecture clusters, religious sites, and modern urban landscapes are dynamically activated via experiential formats such as festival rituals, artistic performances, and evoking emotional memories and achieving the transformation of visitor perceptions from “physical presence → cultural symbols → emotional memory.”Tourists’ participatory behaviors (e.g., ritual interactions and consumption practices) integrate static resources into personal narrative frameworks, triggering a bidirectional reinforcement of identity recognition and cultural resonance. Infrastructure, through functional adaptation (such as intelligent navigation systems and vertical spatial development), ensures the accessibility of resources and the integrity of the overall experience. By seamlessly connecting transit hubs with core attractions via metro stations, the conventional disjointed process of arrival and visitation is transformed into a continuous flow of experience. Moreover, Macau’s casino-hotel clusters employ a unique “organically integrated” infrastructure model, where accommodation, dining, and entertainment facilities construct a closed loop to inherently fulfill physiological needs (e.g., dining and restroom facilities), while channeling tourists into the immersive tourism scenarios. Under the high-density urban conditions of Hong Kong and Macau, land constraints amplify the intensity of hierarchical effects. This is manifested in the “micro-facility networks supporting high-frequency activation of cultural heritage” and “seamless transitions between day and night economic scenarios,” which together establish emotional anchors and a resilient foundation for the destination branding image. According to Figure 3, the complementary relationship among tourism resources, transportation infrastructure, and service quality provides critical support tourist perception. Transportation infrastructure enhance the utilization of tourism resources by minimizing spatial and temporal distances, while the high-density distribution of resources conversely drives the optimization of transportation routes. This interconnection underscores the pivotal role of "mobility convenience" in realizing resource value. Service quality indirectly enhances resource value by improving the comfort of the overall experience, while the complexity of distinctive tourism resources necessitates a higher degree of service quality specialization. This asymmetric relationship indicates that service quality functions primarily as an amplifier of resource value. Moreover, the cross-border location and colonial heritage of Hong Kong and Macau further reinforce the unique efficacy of this tier: Transportation infrastructure operates as convergence hubs for regional synergy, while service quality must perform a bidirectional translation between Eastern and Western cultural symbols and the cultural characteristics of the source market. Based on the findings presented in Figure 3, although the weak connections among service quality, transportation infrastructure, and activity experiences are not statistically significant, they hold important long-tail moderating value. The degree of standardization in service quality directly influences the smoothness of tourists’ experiences, while the capacity for personalized service—by addressing the unique demands of visitors—stimulates innovations in deeper experiential engagement. Information services at transportation nodes optimize tourists’ mobility experiences by providing real-time, accurate transit information, thereby mitigating friction from information asymmetry. In addition, emergency services in response to transportation disruptions alleviate tourists’ negative sentiments through timely and effective measures, which in turn positively impact overall perception. Meanwhile, advancements in infrastructure intelligence—by partially substituting manual services—not only enhance operational efficiency but also, through the integration of technology and service, create a synergistic enhancement pathway characterized by the "hard infrastructure softness", further optimizing the comprehensive tourist experience. The figures in Figure 3 imply that through co-occurrence network analysis, the interaction strengths among the five major factors exhibit a pronounced hierarchical differentiation. The synergistic effects between tourism resources and activity experience, as well as between tourism resources and infrastructure (each with a frequency of 12), constitute the core driving layer. Through the dynamic activation of cultural symbols and functional adaptation, this core layer shapes the emotional depth and resilient foundation of the destination image. In contrast, the complementary interactions between tourism resources and transportation facilities (frequency of 8) and between tourism resources and service quality (frequency of 6) form the supporting layer, which leverages spatiotemporal compression effects and and cognitive interventions to optimize resource accessibility and enhance cultural transmission. Additionally, the complementary relationships among service quality, transportation facilities, and activity experiences, operating at low frequencies (≤3), comprise the long-tail moderating layer. This layer extracts implicit experiential value through the design of emotional touchpoints and embodied technological approaches. In the unique context of Hong Kong and Macau, characterized by high-density and cross-cultural dynamics, these high-frequency mechanisms are further reinforced by spatial constraints and cultural hybridity. This is manifested in the efficient empowerment of micro-infrastructure networks, the seamless transition between day and night scenarios, and the leverage effect of cross-border collaboration, collectively forming a dynamic operational system of "strong core – elastic periphery." 4. Results 4.1 Research Findings This study systematically investigated Japanese tourists’ perceptual characteristics and dynamic shifts in destination image toward China’s Hong Kong and Macau through a multidisciplinary methodology integrating sentiment analysis,high-frequency word analysis, co-occurrence network analysis, and the core-periphery model. Key findings are as follows:(1) High-frequency keyword analysis reveals that Japanese tourists’ perceptions of the destination image in Hong Kong and Macau have diversified, shifting from a singular cultural sightseeing toward a composite cultural-entertainment experience. (2) Tourism resources and activity experiences constitute the core determinants shaping tourist perceptions. While service quality, transportation infrastructure, and urban development, occupy peripheral positions, they play a significant role in enhancing overall tourist satisfaction. (3) The synergistic effect between tourism resources and activity experiences acts as the primary driving force, whereas service quality, transportation infrastructure, and urban development optimize the overall tourist experience through indirect effects. Together, they form a dynamic system of "strong core – elastic periphery," which provides crucial support for improving tourist satisfaction. 4.2 Strengths and Limitations The strengths of this research lie in the integrated application of diverse analytical methods, which significantly enhances its depth. A co-occurrence network was constructed for semantic association analysis, followed by the use of social network analysis to reveal the interaction characteristics among review subjects, thereby the core-periphery structure model was employed to identify the determinants within the overall network, resulting in a comprehensive and in-depth analysis of Japanese tourists’ review data. By analyzing review data across different time periods, the research reveals the dynamic evolution of Japanese tourists’ perceptual characteristics, thereby providing robust data support for the long-term planning and strategic adjustments of tourism destinations. The research has certain limitations: While the study considers the convenience for Japanese tourists traveling to China and acknowledges the strategic importance of Japanese tourists within China's source markets, Hong Kong and Macau’s tourism ecosystems cater to a diverse array of international visitors. Thus, the research based solely on the perspective of Japanese tourists to assess the overall destination image perception of Hong Kong and Macau presents certain limitations. Consequently, future research could further explore the differences in destination image perceptions among tourists from various source markets, thereby advancing our understanding of the overall tourism image characteristics of Hong Kong and Macau. 4.3 Policy implications Firstly, strengthen protection of existing tourism resources, particularly important cultural heritage sites such as Macau's historic Centre, to ensure the continuity of their historical and cultural value. Simultaneously, more tourism projects with rich cultural connotations should be developed by integrating cultural, natural, and entertainment resources with diversified tourism packages designation that cater to the needs of various tourist segments. Additionally, the expansion of cultural festivals and interactive experience programs is recommended. By leveraging technologies such as virtual reality (VR) and augmented reality (AR), tourism activities can be made more engaging and interactive, ultimately enhancing the immersive and experiential quality for tourists. Secondly, implement systematic training programs within the tourism service industry to elevate overall service quality—particularly in hotels and restaurants—in order to provide more personalized and premium services. Concurrently, establish a comprehensive tourism service quality evaluation system that facilitates the timely collection of visitor feedback and supports continuous improvement. Furthermore, regularly conduct tourist satisfaction surveys to identify service deficiencies and implement targeted measures to address these gaps. In addition, increase the frequency and coverage of public transportation to optimize network layout and reduce travel time for tourists. Strengthen traffic management, particularly during peak tourism seasons, to mitigate congestion and enhance the overall travel experience. Leverage intelligent traffic management systems and real-time traffic information dissemination to guide tourists in planning their trips efficiently. Additionally, utilize mobile applications to provide real-time traffic updates and navigation services, ensuring a seamless and convenient travel experience. Finally, enhance the environmental quality of urban public spaces by expanding green infrastructure to improve the aesthetic appeal and overall comfort of the city. Promote the urban cultural-tourism synergistic development, transforming cities into culturally distinctive tourist destinations. Strengthen the preservation and development of cultural activities and historical sites to deepen the city’s cultural capital and enhance its attractiveness to visitors. 5. Conclusion This research employs a comprehensive methodological framework that integrates sentiment analysis, high-frequency word analysis, co-occurrence network analysis, and the core-periphery structural analysis to explore the perceptual characteristics and dynamic evolution of Japanese tourists’ destination image of Hong Kong and Macao. The key conclusions are as follows:(1) Japanese tourists predominantly express positive perceptions toward Hong Kong and Macao, particularly appreciating natural landscapes, historical and cultural heritage, and mobility convenience. However, negative emotions are observed regarding issues such as insufficient tourist facilities, service inconsistencies, and transportation inefficiencies. (2) The focus of Japanese tourists has progressively transitioned from an emphasis on historical and cultural heritage to modern urban landscapes and immersive entertainment experiences. This shift reflects a movement from traditional, static cultural sightseeing towards dynamic, experience-driven tourism consumption. (3) Tourism resources and activity experiences constitute the core components shaping Japanese tourists’ perception of Hong Kong and Macao’s destination image, which always occupy a dominant position. In contrast, elements such as service quality, transportation infrastructure, and urban development, while peripheral, play a significant role in enhancing visitor satisfaction, particularly by improving accessibility and modernizing urban facilities. (4) The dynamic interaction between tourism resources and activity experiences serves as the primary driving force in shaping destination perception. Meanwhile, service quality, transportation infrastructure, and urban development act as supporting factors that indirectly enhance the overall visitor experience. Collectively, these elements form a "strong core—elastic periphery" dynamic interaction system, which plays a vital role in elevating tourist satisfaction.(5) This research pioneers the integration of core-periphery modeling with user-generated content (UGC) analytics, proposing a dual-driven framework of "cultural symbol activation—technological infrastructure empowerment." This approach offers an innovative methodology for managing tourism destination images within high-density urban environments. Declarations Funding: This research was funded by the Natural Science Foundation of Ningbo,grant number 20221JCGY010743 and Shaanxi Provincial Philosophy and Social Science Research Special Program, grant number 2025YB0213 Data Availability Statement : The data can be made available upon request to the corresponding author. These data are not publicly available. Ethics Statement : Not applicable. Conflict of Interest Statement : The authors declare that they have no known financial or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Q.G. and W.K.S. wrote the original draft. M.M.S. contributed methodology, software, and validation. Y.W.W. and Y.Z. handled writing - review and editing. Y.J. conducted investigation, writing - review and editing, and obtained funding. W.T.Y. was responsible for conceptualization, writing - original draft, and writing - review and editing as the corresponding author. All authors reviewed the manuscript. References Levy, S.J. Marketplace Behavior- Its Meaning for Management. New York: Amacom，1978 Crompton, J.L. Motivations for pleasure vacation. Annals of Tourism Research,1979,6(4), 408-424. Fakeye, P.C., & Crompton, J.L.. Image differences between prospective, first-time and repeat visitors to the lower Rio Grande Valley.Journal of Travel Research, 1991,30(2), 10-16. Baloglu, S., & McCleary, K.W.A model of destination image formation. Annals of Tourism Research,1999b,26(4), 868-897. Hunt J D. Image--A Factor in Tourism[M]. Colorado State University, 1971. Stepchenkova, S., & Mills, E.J. Destination image: A meta-analysis of 2000-2007 research. Journal of Marketing & Management,2010,19(6), 575-609. Gallarza, M.G., Saura, I.G., & García, H.C. Destination image: Towards a conceptual framework. Annals of Tourism Research, 2002,29(1), 56-78. . Miao, L., & Wei, W. Consumers’ pro-environmental behavior and the underlying motivations: A comparison between household and hotel settings. International Journal of Hospitality Management 32, 2013,102-112. Buhalis, D. Marketing the competitive destination of the future. Tourism Management,2000,21(1), 97-116. Chen, C.F., & Tsai, D.C. How destination image and evaluative factors affect behavioural intentions?Tourism Management, 2007, 28(4), 1115-1122. Boulding K E. The image: Knowledge in life and society[M]. University of Michigan press, 1961. Camisón C. Total quality management in hospitality: an application of the EFQM model[J]. Tourism management, 1996, 17(3): 191-201. Maital S. " The Experience Economy: Work Is Theatre & Every Business a Stage" by B. Joseph Pine II and James H. Gilmore (Book Review)[J]. Sloan Management Review, 1999, 40(4): 93. Ali F, Kim W G, Li J, et al. Make it delightful: Customers' experience, satisfaction and loyalty in Malaysian theme parks[J]. Journal of destination marketing & management, 2018, 7: 1-11. Crompton J L. Motivations for pleasure vacation[J]. Annals of tourism research, 1979, 6(4): 408-424. Gartner WC， Image Formation Process. J. Travel Tour. Mark. 2(2/3): 1993,191-215. Pizam A, Sussmann S. Does nationality affect tourist behavior?[J]. Annals of tourism research, 1995, 22(4): 901-917. Avraham E. Destination marketing and image repair during tourism crises: The case of Egypt[J]. Journal of Hospitality and Tourism Management, 2016, 28: 41-48. Ritchie B W, Jiang Y. A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management[J]. Annals of tourism Research, 2019, 79: 102812. Line N D, Hanks L. The effects of environmental and luxury beliefs on intention to patronize green hotels: The moderating effect of destination image[J]. Journal of Sustainable Tourism, 2016, 24(6): 904-925. Budeanu A, Miller G, Moscardo G, et al. Sustainable tourism, progress, challenges and opportunities: an introduction[J]. Journal of cleaner production, 2016, 111: 285-294. Pike S, Page S J. Destination Marketing Organizations and destination marketing: A narrative analysis of the literature[J]. Tourism management, 2014, 41: 202-227. Özdemir C, Yolal M. Cross-cultural tourist behavior: An examination of tourists’ behavior in guided tours[J]. Tourism and Hospitality Research, 2017, 17(3): 314-324. Lee Y K, Pei F, Ryu K, et al. Why the tripartite relationship of place attachment, loyalty, and pro-environmental behaviour matter?[J]. Asia Pacific Journal of Tourism Research, 2019, 24(3): 250-267. Wang R, Hao J X, Law R, et al. Examining destination images from travel blogs: A big data analytical approach using latent Dirichlet allocation[J]. Asia Pacific Journal of Tourism Research, 2019, 24(11): 1092-1107. Park D, Lee G, Kim W G, et al. Social network analysis as a valuable tool for understanding tourists’ multi-attraction travel behavioral intention to revisit and recommend[J]. Sustainability, 2019, 11(9): 2497. Ren G, Hong T. Investigating online destination images using a topic-based sentiment analysis approach[J]. Sustainability, 2017, 9(10): 1765. Marine-Roig E. Destination image analytics through traveller-generated content[J]. Sustainability, 2019, 11(12): 3392. Zhang Y, Gao J, Cole S, et al. How the spread of user-generated contents (UGC) shapes international tourism distribution: Using agent-based modeling to inform strategic UGC marketing[J]. Journal of Travel Research, 2021, 60(7): 1469-1491. Yamagishi K, Canayong D, Domingo M, et al. User-generated content on Gen Z tourist visit intention: a stimulus-organism-response approach[J]. Journal of Hospitality and Tourism Insights, 2024, 7(4): 1949-1973. Kirilenko A P, Stepchenkova S O, Hernandez J M. Comparative clustering of destination attractions for different origin markets with network and spatial analyses of online reviews[J]. Tourism management, 2019, 72: 400-410. Wong, C. U. I., & Qi, S. Tracking the evolution of a destination’s image by text-mining online reviews - the case of Macau. Tourism Management Perspectives,2017,23, 19–29. Jimenez-Barreto J, Sthapit E, Rubio N, et al. Exploring the dimensions of online destination brand experience: Spanish and North American tourists' perspectives[J]. Tourism Management Perspectives, 2019, 31: 348-360. Lian, T., & Yu, C. Representation of online image of tourist destination: A content analysis of Huangshan. Asia Pacific Journal of Tourism Research,2017, 22(10), 1063–1082. Mak, A. H. N.Online destination image: Comparing national tourism organisation’s and tourists’ perspectives.Tourism Management,2017, 60, 280–297 Lam, J. M., Ismail, H., & Lee, S.From desktop to destination: User-generated content platforms, co-created online experiences, destination image and satisfaction. Journal of Destination Marketing & Management,2020, 18, 100490. Choi, S., Lehto, X. Y., & Morrison, A. M. Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management,2007, 28(1), 118–129. Xiang, Z.Role of social media in online travel information search. Tourism Management,2010, 31(2), 179–188. Baloglu, S., & Brinberg, D. Affective images of tourism destinations. Journal of Travel Research,1997, 35(4), 11–15. Wang, C., & Hsu, M. The relationships of destination image, satisfaction, and Behavioral Intentions: An integratedmodel. Journal of Travel and Tourism Marketing, 201027(8), 829–843. Selin S, Chavez D. Develo an evolutionary tourism partnership model[J]. Annals of tourism research, 1995, 22(4): 844-856. McKercher B, Shoval N, Ng E, et al. Using GPS data to compare first-time and repeat visitors to Hong Kong[J]. Tourism Geographies, 2012, 14(1): 147-161. Dredge D. Policy networks and the local organisation of tourism[J]. Tourism management, 2006, 27(2): 269-280. Li Y, Chen Y, Fan Y, et al. Dynamic network modeling of gut microbiota during Alzheimer’s disease progression in mice[J]. Gut Microbes, 2023, 15(1): 2172672. Park D, Lee G, Kim W G, et al. Social network analysis as a valuable tool for understanding tourists’ multi-attraction travel behavioral intention to revisit and recommend[J]. Sustainability, 2019, 11(9): 2497. Ritchie B W, Jiang Y. A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management[J]. Annals of tourism Research, 2019, 79: 102812. Li M, Wang X, Gao K, et al. A survey on information diffusion in online social networks: Models and methods[J]. Information, 2017, 8(4): 118. Панг Б, Ли Л. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval[J]. 2008. 樋口耕一. A two-step approach to quantitative content analysis: KH Coder tutorial using Anne of Green Gables (Part I)[J]. 立命館産業社会論集= 立命館産業社会論集, 2016, 52(3): 77-91. Marine-Roig E, Clavé S A. Tourism analytics with massive user-generated content: A case study of Barcelona[J]. Journal of Destination Marketing & Management, 2015, 4(3): 162-172. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6637197","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466305716,"identity":"9ff828a4-4f9d-47f9-af32-10f7b71cdfa4","order_by":0,"name":"Qiyuan Gong","email":"","orcid":"","institution":"Xi'an Aeronautical University","correspondingAuthor":false,"prefix":"","firstName":"Qiyuan","middleName":"","lastName":"Gong","suffix":""},{"id":466305717,"identity":"a27368bb-9d4d-4b6c-bf31-6f19d1b695ef","order_by":1,"name":"Mengmeng Sun","email":"","orcid":"","institution":"Chang'an University","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Sun","suffix":""},{"id":466305718,"identity":"c85a0b6f-e5ca-479a-8b5c-564645a9d3a0","order_by":2,"name":"Wenkai Shi","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Wenkai","middleName":"","lastName":"Shi","suffix":""},{"id":466305719,"identity":"b8748086-fc92-433f-a77f-a8fc1a7808e2","order_by":3,"name":"Yuwei Wang","email":"","orcid":"","institution":"Xi'an Aeronautical University","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Wang","suffix":""},{"id":466305720,"identity":"192cea26-633c-4ff4-a2f9-d99e70086d07","order_by":4,"name":"Yang Zhang","email":"","orcid":"","institution":"Xi'an Aeronautical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":466305721,"identity":"0dabc58f-fddd-4b26-b539-7ec8b34ace3d","order_by":5,"name":"Wangtun Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBADOQYJBgMGBjZi1R9gYDAmXUtiA9FaDI6fPfz6Y9ud9P7ZzRsYPpQdZuCf3UBAy5m8NIuDbc9yZ9w5VsA449xhBok7B/BrMTuQY2ZwsO1w7gaJHANm3rbDDAYSCQS0nH8D1pJuANLylygtN3KMHwC1JIC1MBKjxf7GGzOGM+cOG864kVZwsOdcOo/EDQJaJPtzjD9UlB2W55+RvPHBjzJrOf4ZBLQAAZsEjHUAiHkIqgcC5g/EqBoFo2AUjIIRDABuZkjsVRCgoQAAAABJRU5ErkJggg==","orcid":"","institution":"Chang'an University","correspondingAuthor":true,"prefix":"","firstName":"Wangtun","middleName":"","lastName":"Yang","suffix":""},{"id":466305722,"identity":"fe2e1764-b2bb-42df-9677-63b42e4d665c","order_by":6,"name":"Ying Jing","email":"","orcid":"","institution":"NingboTech University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Jing","suffix":""}],"badges":[],"createdAt":"2025-05-11 02:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6637197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6637197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84282431,"identity":"573c67ec-4f75-448a-a1ea-24c386b4648c","added_by":"auto","created_at":"2025-06-10 06:58:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":957381,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the research area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6637197/v1/7d4483fea2b947dd42948dd7.jpeg"},{"id":84281403,"identity":"f27e9b0f-c2ea-4ceb-ae3b-ee7a1af76f64","added_by":"auto","created_at":"2025-06-10 06:50:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123691,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3 Mechanism of Influencing Factors\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6637197/v1/b610fff941f3ec9faf3eaeb5.png"},{"id":90732221,"identity":"acada8e4-53c5-4786-b85a-34fc45ab1530","added_by":"auto","created_at":"2025-09-06 17:31:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1563286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6637197/v1/f1ad9645-a0f9-4c12-a872-b2be5487eff2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceptions of Destination Image among Japanese Tourists toward the Hong Kong and Macau Regions of China: An Analysis Based on Online Reviews","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe destination image plays a decisive role in tourism decisions.The concept of \u0026quot;image\u0026quot; refers to the the overall impression that consumers have of a brand or product, encompassing cognition, emotion, and attitude (Levy, 1978) \u003csup\u003e[1]\u003c/sup\u003e. In contexts of information overload, this image becomes a decisive factor in the decision-making process. Destination image, defined as an individual\u0026rsquo;s cognition, beliefs, and feelings regarding a specific destination (Crompton, 1979; Fakeye \u0026amp; Crompton, 1991; Baloglu \u0026amp; McClery, 1999b) \u003csup\u003e[2\u0026ndash;4]\u003c/sup\u003e. This concept is applicable both to consumer goods markets and to the tourism industry. Hunt (1971) \u003csup\u003e[5]\u003c/sup\u003e first introduced the notion of destination image into tourism research, establishing the perception of tourism destination image as a a critical academic focus (Stepchenkova \u0026amp; Mills, 2010; Gallarza et al., 2002) \u003csup\u003e[6\u0026ndash;7]\u003c/sup\u003e. Tourism destinations compete through image-building to attract visitors, with stronger positive impressions correlating to higher likelihoods of visitation. Indeed, destination image is recognized as a key determinant influencing travel decisions (Mayo, 1975; Beerli \u0026amp; Martin, 2004; Chen \u0026amp; Tsai, 2007) \u003csup\u003e[8\u0026ndash;10]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on destination image has predominantly focused on the interplay among the environment, the destination, and the tourist. Within this framework, image\u0026mdash;as a synthesis of subjective values and accumulated knowledge\u0026mdash;profoundly influences tourist decision-making and their interaction with the external environment (Boulding, 1956) \u003csup\u003e[11]\u003c/sup\u003e. By cultivating a superior environment, tourism destinations can enhance their image, thereby attracting more visitors and initiating a virtuous cycle from environmental improvement to increased tourist attraction. By developing a favorable environment, tourism destinations can enhance their image and, in turn, attract more visitors, creating a virtuous cycle from environmental improvement to tourist attraction. When selecting a destination, tourists rely on the destination image to assess factors such as the environmental conditions, safety, service quality, and peer evaluations. In light of evolving tourist preferences and intensifying competition among emerging destinations, the importance of total quality management in the tourism industry has become increasingly evident (Camison, 1996) \u003csup\u003e[12]\u003c/sup\u003e. Moreover, consumer experience has been found to be more compelling than the intrinsic features of products or services (Pine \u0026amp; Gilmore, 1999) \u003csup\u003e[13]\u003c/sup\u003e. Consequently, destination managers must maintain high-quality facilities, human resources, and other consumer-focused services to provide a pleasant customer experience (Ali et al., 2018) \u003csup\u003e[14]\u003c/sup\u003e. This strategy not only addresses tourists\u0026rsquo; heightened expectations regarding environmental and service quality but also encourages continuous improvements, establishing a positive feedback loop among the environment, the destination, and the tourists. These theoretical frameworks provide a critical foundation for subsequent research on destination image perception.\u003c/p\u003e\n\u003cp\u003eThe research on destination image perception has achieved multi-dimensional paradigm evolution.Destination Image Perception is defined as the aggregate of an individual\u0026rsquo;s beliefs, thoughts, and impressions regarding a particular destination. In the context of tourism, destination image perception reveals the degree to which tourists value and evaluate destination attributes\u0026mdash;such as natural scenery, cultural heritage, and service quality. Over half a century of theoretical refinement and methodological innovation, research on destination image perception has exhibited a distinct paradigm shift. Initial studies focused on cognitive-affective composite dimensions, with Crompton\u0026rsquo;s (1979)\u003csup\u003e[15]\u003c/sup\u003e dual-component model (cognitive and affective) and Gartner\u0026rsquo;s (1993)\u003csup\u003e[16]\u003c/sup\u003e three-stage formation mechanism serving as the core, establishing a foundational measurement system based on questionnaires and factor analysis (Pizam \u0026amp; Sussmann, 1995)\u003csup\u003e[17]\u003c/sup\u003e. With the increasing complexity of tourism systems amid globalization, the research perspective expanded in the early 21st century to encompass operational management practices, resulting in the development of four core branches:(1) Crisis-Driven image restoration mechanisms (Avraham, 2016; Ritchie \u0026amp; Jiang, 2019)\u003csup\u003e[18-19]\u003c/sup\u003e; (2) Eco-Community synergy models oriented toward sustainable development (Line \u0026amp; Hanks, 2016; Budeanu et al., 2016)\u003csup\u003e[20-21]\u003c/sup\u003e; (3) dynamic evaluation frameworks for destination brand equity (Pike \u0026amp; Page, 2014)\u003csup\u003e[22]\u003c/sup\u003e; and (4) the moderating effects of cultural identity on tourist decision-making (Ozdemir \u0026amp; Yolal, 2016; Lee et al., 2019b)\u003csup\u003e[23-24]\u003c/sup\u003e. Furthermore, with the rapid advancement of digital technology, research has gradually shifted from traditional qualitative analytical methods to data-driven approaches, such as the utilization of big data and social media (Wang et al., 2019; Park et al., 2019; Ren \u0026amp; Hong, 2017)\u003csup\u003e[25-27]\u003c/sup\u003e and user-generated content (UGC) (Marine-Roig, 2017; Zhang et al., 2021;Yamagishi K et al.,2024)\u003csup\u003e[28-30]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe digital technology revolution has reshaped the methodology of tourism image research.Online user-generated content (UGC) platforms have become increasingly vital for destination management and promotion (Kirilenko et al., 2019) \u003csup\u003e[31]\u003c/sup\u003e. UGC not only shapes the online destination imagery but also influences tourists\u0026apos; perceptions during their browsing process, thereby motivating their travel decisions and destination choices (Wong \u0026amp; Qi, 2017) \u003csup\u003e[32]\u003c/sup\u003e. The continuous advancement of technology has placed destination marketing organizations (DMOs) in a highly competitive environment, prompting them to manage and innovate their services to enhance their online destination imagery (Jimenez-Barreto et al., 2019) \u003csup\u003e[33]\u003c/sup\u003e. The advancement of technology enables tourists to create and share their impressions and reviews of destinations online (Lian \u0026amp; Yu, 2017; Mak, 2017)\u003csup\u003e[34-35]\u003c/sup\u003e. Travelers\u0026apos; first impressions and decisions increasingly rely on reviews from others who have visited the destination (Lam et al., 2020) \u003csup\u003e[36]\u003c/sup\u003e. Consequently, online digital information has emerged as a key reference for destination managers in shaping destination image (Choi et al., 2007; Xiang, 2010) \u003csup\u003e[37-38]\u003c/sup\u003e, while also significantly influencing tourists\u0026apos; decisions and behaviors (Baloglu \u0026amp; Brinberg, 1997; Wang \u0026amp; Hsu, 2010) \u003csup\u003e[39-40]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSocial network analysis has become a key paradigm for analyzing complex tourism systems.The theoretical trajectory of SNA in tourism research has undergone three distinct phases: Early studies focused on describing the structures of tourism collaboration networks(Selin \u0026amp; Chavez, 1995), relying on small-sample static analyses. With the widespread adoption of tools like UCINET, research extended to various contexts, including spatial patterns of tourism flows(McKercher et al., 2012)\u003csup\u003e[42]\u003c/sup\u003eand stakeholder power networks (Dredge, 2006)\u003csup\u003e[43]\u003c/sup\u003e. In recent years, SNA has deeply integrated with big data and artificial intelligence technologies, leading to methodological advancements such as dynamic network modeling (Li et al., 2023)\u003csup\u003e[44]\u003c/sup\u003e and multimodal data fusion( Park et al., 2019)\u003csup\u003e[45]\u003c/sup\u003e. These advancements have unveiled deeper mechanisms, like crisis propagation pathways (Ritchie \u0026amp; Jiang, 2019)\u003csup\u003e[46]\u003c/sup\u003eand social media sentiment diffusion (Li et al., 2017)\u003csup\u003e[47]\u003c/sup\u003e. Current research has established theoretical frameworks, such as network indicator evaluation systems (e.g., centrality-vulnerability correlation models). However, challenges persist, including the lack of longitudinal data, insufficient sensitivity to cultural contexts, and debates over data ethics.\u003c/p\u003e\n\u003cp\u003eJapan serves as one of the most significant source markets for Hong Kong and Macau, characterized by strong purchasing power and substantial contributions to their tourism-driven economies.Additionally, the cultural connections and distinctions between Japan and China provide a unique context for understanding Japanese tourists\u0026apos; perceptions of destination image in Hong Kong and Macao. Investigating their perceptions aids in comprehending the needs and preferences of this demographic, while offering cross-cultural comparative value for studies on other international markets. However, existing research predominantly focus on European and American source markets, lacking targeted exploration of Japanese tourists\u0026apos; dynamic perceptions in densely populated urban destinations like Hong Kong and Macao. A notable theoretical gap persists in understanding the synergistic mechanisms between cultural symbolism and technological infrastructure in shaping destination perceptions. Furthermore, the application of social network analysis (SNA) in tourism destination image perception research remains nascent, presenting substantial underexplored potential for analyzing the dynamic changes and influencing mechanisms of specific destination image perceptions.\u003c/p\u003e\n\u003cp\u003eThis study delves into the perceptual characteristics and temporal dynamics of Japanese tourists\u0026rsquo; destination image perceptions of China\u0026rsquo;s Hong Kong and Macau, aiming to identify key influencing factors and their mechanisms to enhance these regions\u0026apos; competitiveness in the international tourism market. Employing sentiment analysis, high-frequency word analysis, co-occurrence network analysis, and a core-periphery model, the research comprehensively analyzes Japanese tourists\u0026apos; destination image perceptions from multiple perspectives, revealing dynamic changes in these perceptions, and identifying key influencing factors and interaction mechanisms. The findings not only provide theoretical support and practical guidance for the management and optimization of tourism destinations in Hong Kong and Macau,but also establishes a reference paradigm for studying perceptions of Chinese tourism destinations among international tourists.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main contributions of this research can be summarized as follows:(1)It reveals the perceptual characteristics of Japanese tourists toward the tourism image of Hong Kong and Macau across different time periods, deepening the understanding of the evolution of tourist perceptions over time and providing strong data support for long-term planning and strategic adjustments of tourism destinations.(2)The integrated use of diverse analytical methods enhances the depth of the research. Initially, a co-occurrence network is constructed for semantic association analysis, followed by the application of social network analysis methods to uncover interaction characteristics among review subjects, and finally, a core-periphery structure model is employed to identify key influencing factors within the overall network. This approach facilitates a comprehensive and in-depth analysis of Japanese tourists\u0026apos; review data. (3)The research identifies the key factors and mechanisms influencing Japanese tourists\u0026apos; perceptions of the tourism image of Hong Kong and Macau.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe structure of this paper is as follows: Section 1 is the introduction; Section 2 introduces the overview of the research area and research methods; Section 3 explores Japanese tourists\u0026apos; perceptions of the tourism image of Hong Kong and Macau through text analysis; Section 4 delves into the research results, advantages and limitations, and proposes corresponding policy recommendations; Section 5 is the conclusion.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e2.1 Study site\u003c/p\u003e\n\u003cp\u003eAs observed in Figure 1, Hong Kong and Macau, as China\u0026apos;s two Special Administrative Regions (SARs), are situated in the Pearl River Delta, forming a unique cross-border tourism city cluster. Governed under the \u0026ldquo;One Country, Two Systems\u0026rdquo; framework, both regions exhibit hybrid governance models blending post-colonial legacies with contemporary Chinese urbanism. Covering a combined area of 3106 km\u0026sup2; and accommodating a population of 7.8 million, Hong Kong and Macau represent quintessential examples of ultra-high-density urban environments, with population densities of 6,801 and 21,340 persons per square kilometer, respectively. These characteristics render them ideal case studies for examining tourism experiences within spatially constrained settings. The Hong Kong Special Administrative Region, located east of the Pearl River estuary, boasts a comprehensive transportation network, rich historical and cultural heritage, and numerous tourist attractions, earning it the \u0026ldquo;shopping paradise,\u0026rdquo;that harmonizes Eastern and Western cultural influences. Macau Special Administrative Region, situated on the western side of the Pearl River estuary, is renowned for its gaming industry, abundant historical sites, and modern entertainment facilities, creating a distinctive cultural identity. Annually, both regions receive an average of 65.8 million visitors (rebounded to 58% of pre-pandemic levels in 2022), with Japanese tourists accounting for 8.7% of international arrivals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2 Data Sources\u003c/p\u003e\n\u003cp\u003eThis study utilizes online reviews and video data published by Japanese tourists in Hong Kong SAR and Macau SAR, China, from 2009 to 2023. Textual data was sourced from the followingplatforms:4travel:https://4travel.jp/os_area_country-china.html;Tripadvisor:https://www.tripadvisor.jp.Video data is extracted from YouTube:https://www.youtube.com. A hybrid approach combining automated algorithms and manual verification was applied to clean and filter the raw data, including the removal of duplicate entries, irrelevant or non-substantive reviews, and content unrelated to the research focus. Ultimately, 10,778 valid reviews are retained for analysis.\u003c/p\u003e\n\u003cp\u003e2.3Period division\u003c/p\u003e\n\u003cp\u003eThe research collected and analyzed the online comments posted by Japanese tourists in Hong Kong and Macao, China from 2009 to 2023. Considering the policy cycle, key events and the evolving trends of tourists\u0026apos; perceptions comprehensively, the period from 2009 to 2023 was divided into three stages: The periods from 2009 to 2013 (the period of focus on cultural heritage), 2014 to 2018 (the period of transformation towards modernization and entertainment), and 2019 to 2023 (the period of crisis response and technological empowerment) are hereinafter referred to as the early, middle and late stages respectively.\u003c/p\u003e\n\u003cp\u003e2.4 Methods\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this research, the sentiment analysis module of the ROST CM software is calibrated with a localized Japanese lexicon to ensure accuracy in cross-language analysis, ensuring consistency in classification. The ROST CM software is employed to perform sentiment analysis on the collected online review texts, categorizing the comments into three sentiment levels: positive, neutral, and negative\u003csup\u003e[48]\u003c/sup\u003e. To refine granularity, positive and negative sentiments are further subcategorized, enabling precise identification of tourists\u0026rsquo; affective orientations toward various aspects of Hong Kong and Macau.\u003c/p\u003e\n\u003cp\u003eThe corpus analysis tool KH Coder is an open-source software designed for quantitative text analysis and text mining. As a semantic analysis tool, it offers comprehensive functionality, including frequent updates, and supports simplified management. Notably, KH Coder provides visual mapping capabilities that intuitively elucidate intrinsic associations between high-frequency terms and themes\u003csup\u003e[49]\u003c/sup\u003e. In this research, KH Coder is utilized for data processing of the self-constructed corpus, employing high-frequency word analysis and co-occurrence network analysis.\u003c/p\u003e\n\u003cp\u003eHigh-frequency word analysis identifies prominent themes and focal points in textual data by statistically extracting the most recurrent terms. In this research, KH Coder software is employed to conduct frequency statistics on the collected online reviews, extract high-frequency vocabulary, and categorize them based on perceptual dimensions and sentiment analysis results. Co-occurrence network analysis is a method used to uncover the relationships between words by examining their simultaneous appearances within a given context. By constructing a co-occurrence matrix of high-frequency terms and visualizing them as interactive network graphs, the analysis quantified the strength of associations between different terms, revealing the interconnections among various perceptual elements mentioned in tourists\u0026rsquo; reviews.\u003c/p\u003e\n\u003cp\u003eThe core-periphery model, a method within social network analysis (SNA)\u003csup\u003e[50]\u003c/sup\u003e, is designed to identify core and peripheral elements within a network. This model is particularly suitable for analyzing hierarchical structures, effectively distinguishing between dominant factors (core) and auxiliary factors (periphery) within the high-density resources of Hong Kong and Macau. In this research, Ucinet is utilized to conduct core-periphery analysis on the co-occurrence network, aiming to identify the core and peripheral elements influencing tourist perceptions across different time periods.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Sentiment Analysis\u003c/p\u003e\n\u003cp\u003eThe sentiment analysis conducted on the textual data categorized emotions into three levels: positive, neutral, and negative, with further subdivisions within positive and negative sentiments, as detailed in Table 1.Japanese tourists\u0026apos; perceptions of Hong Kong and Macau predominantly exhibited positive sentiments, comprising 91.39% of the total comments, with 36.49% reflecting highly positive sentiments. Neutral and negative sentiments represented 2.17% and 6.44%, respectively, indicating a strong reputation of these regions among Japanese tourists, with the vast majority having positive experiences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of the content within positive sentiments revealed appreciation for aspects such as natural landscapes (\u0026quot;abundant natural scenery viewed from surrounding skyscrapers\u0026quot;), cultural experiences(\u0026quot;locations steeped in historical ambiance\u0026quot;), shopping and entertainment(\u0026quot;diverse shopping malls and entertainment options\u0026quot;), service quality and management (\u0026quot;comprehensive and attentive service across all roles\u0026quot;), transportation efficiency(\u0026quot;free shuttle buses from the airport; highly convenient transit\u0026quot;), and urban development (\u0026quot;ongoing infrastructure projects and dynamic city growth\u0026quot;).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, negative sentiments primarily focused on issues related to attraction facilities and experiences (\u0026quot;although crowded, it\u0026apos;s just for photos; it feels somewhat desolate, and many amusement facilities are closed\u0026quot;), travel transportation and accessibility (\u0026quot;Macau Tower\u0026rsquo;s poor connectivity; hard to reach on foot from major tourist spots\u0026quot;), service quality and management (\u0026quot; uneven hotel service quality; some staff have indifferent attitudes\u0026quot;), cultural and environmental adaptation (\u0026quot; Macau\u0026apos;s historical sites like the Ruins of St. Paul\u0026rsquo;s require time to navigate cultural differences\u0026quot;), language barriers (\u0026quot;language barriers in Macau\u0026apos;s casinos pose some difficulties\u0026quot;), and tourism planning and information gaps (\u0026quot;lack of information may lead to missing key attractions or activities\u0026quot;).\u003c/p\u003e\n\u003cp\u003eTable 1. Sentiment Analysis Results\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"392\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eEmotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003ePositive emotions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e9844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e91.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeutral emotions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative emotions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBreakdown of Positive Emotions:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow(0-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate(10-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh(20 and above)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBreakdown of Negative Emotions:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow(-10-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate(-20--10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh(-20 and below)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3.2 High-Frequency Word Analysis\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, this research utilized the word frequency analysis module of KH Coder software to examine high-frequency terms across three distinct periods: 2009\u0026ndash;2013, 2014\u0026ndash;2018, and 2019\u0026ndash;2023. These terms are systematically categorized based on perceptual elements and sentiment analysis results into six primary categories: \u0026mdash;tourism resources, activity experiences, service provision, transportation accessibility, infrastructure development level, and others\u0026mdash;based on perceptual elements and sentiment analysis outcomes. \u0026nbsp;Table 2 reflects the focus of Japanese tourists on tourism resources in Hong Kong and Macau has undergone significant changes over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the early period (2009\u0026ndash;2013), high-frequency words included \u0026quot;Macau,\u0026quot; \u0026quot;plazas,\u0026quot; \u0026quot;churches,\u0026quot;\u0026quot;architectural heritage,\u0026quot; and \u0026quot;casinos,\u0026quot; reflecting a strong interest in Macau\u0026apos;s historical culture and religious architecture, as well as its unique architectural style and gambling culture. During the mid-period (2014\u0026ndash;2018), the focus shifted to terms like \u0026quot;Hong Kong,\u0026quot; \u0026quot;Macau,\u0026quot; \u0026quot;travel,\u0026quot; \u0026quot;plazas,\u0026quot; \u0026quot;architecture,\u0026quot; \u0026quot;night view,\u0026quot; and \u0026quot;parks,\u0026quot; indicating a growing awareness of Hong Kong, a deeper overall recognition of tourism resources in both regions, and a growing interest in entertainment and leisure resources such as night views and parks. In the later period (2019\u0026ndash;2023), terms like \u0026quot;Hong Kong,\u0026quot; \u0026quot;Macau,\u0026quot;\u0026quot;travel,\u0026quot; \u0026quot;plazas,\u0026quot; and \u0026quot;architecture,\u0026quot; remained central. With the emergence of words like \u0026quot;landmarks\u0026quot;\u0026quot; and \u0026quot;Disneyland.\u0026quot;, it shows tourists\u0026apos; expectations for the diversity of tourism resources.At the same time, the emergence of attractions and entertainment facilities in Hong Kong and Macao highlights the brand influence of Hong Kong Disneyland, and the absence of specific attraction names in Macau indicates that further efforts are needed in destination branding.\u003c/p\u003e\n\u003cp\u003eAs presented in Table 2, the focus of Japanese tourists on activity experiences has shifted from passive sightseeing to immersive and diversified engagement. In the early period (2009\u0026ndash;2013), high-frequency words such as \u0026quot;travel,\u0026quot; \u0026quot;atmosphere,\u0026quot; \u0026quot;photography,\u0026quot; and \u0026quot;impressions\u0026quot; indicated that tourists prioritized the overall ambient experiences of their trips and valued commemorative photography at landmarks. During the mid-period (2014\u0026ndash;2018), the emergence of terms like \u0026quot;impressions,\u0026quot; \u0026quot;amusement facilities,\u0026quot; and \u0026quot;performance\u0026quot; signified an increasing interest in experiential tourism. This shift suggests that tourists gradually moved from superficial sightseeing toward more immersive travel experiences. In the later period (2019\u0026ndash;2023), while terms like \u0026quot;atmosphere,\u0026quot; \u0026quot;photography,\u0026quot; \u0026quot;amusement facilities,\u0026quot; and \u0026quot;performance\u0026quot; remained prominent, new keywords such as \u0026quot;movie-themed events,\u0026quot; \u0026quot;concert tours,\u0026quot; \u0026quot;hiking,\u0026quot; and \u0026quot;bars\u0026quot; emerged. Highlights growing demand for diversification and personalization of tourist activities, along with an increasing interest in cultural and entertainment experiences. Beyond traditional shopping and sightseeing, Japanese tourists in Hong Kong engaged in movie-themed activities, attended concert touring performances, while in Macau, they explored historic districts through hiking or immersed in the nightlife in bars. These trends highlight a sustained rise in demand for cultural and entertainment-related tourism activities.\u003c/p\u003e\n\u003cp\u003eBased on the results shown in Table 2, Japanese tourists\u0026apos; focus on service quality has gradually expanded from basic accommodation services to dining services and other value-added offerings. In the early period (2009\u0026ndash;2013), high-frequency words such as \u0026quot;hotels\u0026quot; and \u0026quot;complimentary services\u0026quot; indicate that tourists primarily emphasized accommodation services. During the mid-period (2014\u0026ndash;2018), the emergence of additional terms like \u0026quot;hotels,\u0026quot; \u0026quot;complimentary services,\u0026quot; \u0026quot;slightly,\u0026quot; and \u0026quot;restaurants\u0026quot; suggests a growing concern for local service quality. Compared to the early period, when the focus was mainly on basic accommodation, there was an increasing interest in the dining industry. In the later period (2019\u0026ndash;2023), the inclusion of the keyword \u0026quot;gifts\u0026quot; reflects an elevated expectation for high-quality service. Tourists not only valued basic accommodation and dining services but also placed greater emphasis on supplementary services offered by hotels and restaurants, such as \u0026quot;Welcome gifts,\u0026quot; \u0026quot;customized services,\u0026quot;This shift highlights an increasing awareness of service details and rising expectations, leading to more refined and comprehensive evaluations of overall service quality.\u003c/p\u003e\n\u003cp\u003eTable 2 provides evidence that Japanese tourists\u0026apos; focus on transportation infrastructure has gradually expanded from basic public transportation options to concerns about efficiency and accessibility. In the early period (2009\u0026ndash;2013), high-frequency words such as \u0026quot;buses\u0026quot; and \u0026quot;taxis\u0026quot; reflect that tourists primarily relied on buses and taxis as their main modes of transportation. During the mid-period (2014\u0026ndash;2018), the emergence of additional terms like \u0026quot;buses,\u0026quot; \u0026quot;ferries,\u0026quot; \u0026quot;rush hours,\u0026quot; and \u0026quot;escalators\u0026quot; suggests an increase in transportation options, including ferries alongside buses and taxis, thereby enriching tourists\u0026apos; travel experiences. However, the growing variety of transportation modes also led to an increase in congestion issues, reflecting a rising concern about traffic conditions.In the later period (2019\u0026ndash;2023), terms such as \u0026quot;buses,\u0026quot; \u0026quot;ferries,\u0026quot; \u0026quot;rush hours,\u0026quot; and \u0026quot;escalators\u0026quot; remained prominent, but the frequent occurrence of \u0026quot;entry\u0026quot; indicates that tourists\u0026rsquo; concerns extended beyond the diversity and coverage of transportation modes. They began to place greater emphasis on operational efficiency and convenience, including the seamless of transfers at transport hubs, traffic management at scenic area entrances, and measures for handling peak-hour congestion. This shift underscores tourists\u0026apos; increasing expectations for a seamless and efficient transportation experience, highlighting the importance of connectivity and fluidity in travel.\u003c/p\u003e\n\u003cp\u003eThe results in Table 2 suggest that Japanese tourists\u0026apos; perceptions of infrastructure in Hong Kong and Macau exhibit clear, stage-specific changes. In the early period (2009\u0026ndash;2013), tourists primarily focused on the architectural details and spatial layouts, with high-frequency words such as \u0026ldquo;staircases,\u0026rdquo; \u0026ldquo;spacious,\u0026rdquo; and \u0026ldquo;interior\u0026rdquo; reflecting their interest in iconic structures like Victoria Peak in Hong Kong and the Ruins of St. Paul\u0026rsquo;s in Macau. At this stage, their perceptions were largely derived from experiences at single attractions. During the mid-period (2014\u0026ndash;2018), the high-frequency terms shifted to include words like \u0026ldquo;staircases,\u0026rdquo; \u0026ldquo;centers,\u0026rdquo; \u0026ldquo;pickup points,\u0026rdquo; and \u0026ldquo;spacious,\u0026rdquo;indicating an increased attention to urban centers and transportation nodes. For example, bustling commercial hubs such as Causeway Bay and Tsim Sha Tsui in Hong Kong, along with large integrated resorts like The Venetian Macao in Macau, made transportation convenience and commercial facilities critical components of the tourist experience. At this juncture, tourists\u0026rsquo; perceptions began to expand from individual buildings to comprehensive experiences within specific urban areas. In the later period (2019\u0026ndash;2023), high-frequency words such as \u0026ldquo;many,\u0026rdquo; \u0026ldquo;skyscrapers,\u0026rdquo; \u0026ldquo;area,\u0026rdquo; \u0026ldquo;centers,\u0026rdquo; and \u0026ldquo;facilities\u0026rdquo; remained prominent, reflecting a broad-based focus on modern urban development in Hong Kong and Macau. Tourists showed a strong interest in mega infrastructure projects like the Hong Kong\u0026ndash;Zhuhai\u0026ndash;Macao Bridge, as well as in modern shopping centers and luxury hotels, with heightened expectations for the overall environment and urban landscape of these destinations. At this stage, perceptions evolved from being centered on individual structures to encompassing entire regional complexes, signifying an upgraded holistic evaluation of urban aesthetics. Assessments of urban development became more detailed and integrated, with an expanded dimensionality of perception. This transition from a micro- to a macro-level focus underscores the continuously rising expectations of Japanese tourists regarding their overall tourism experience in the region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs seen in Table 2, Japanese tourists\u0026rsquo; perceptions regarding other dimensions indicate a continuously rising overall satisfaction with tourism in Hong Kong and Macau. In the early period(2009\u0026ndash;2013), high-frequency words such as \u0026ldquo;world,\u0026rdquo; \u0026ldquo;good,\u0026rdquo; and \u0026ldquo;many\u0026rdquo; reflected a generally positive overall impression of the destination. During the mid-period(2014\u0026ndash;2018), the high-frequency words shifted to \u0026ldquo;good,\u0026rdquo; \u0026ldquo;world,\u0026rdquo; \u0026ldquo;Japan,\u0026rdquo; and \u0026ldquo;delightful,\u0026rdquo; signaling a marked improvement in tourists\u0026rsquo; pleasurable experiences and an enhanced recognition of the convenience of tourism services. In the later period(2019\u0026ndash;2023), while terms like \u0026ldquo;world,\u0026rdquo; \u0026ldquo;good,\u0026rdquo; \u0026ldquo;Japan,\u0026rdquo; and \u0026ldquo;delightful\u0026rdquo; remained prominent, the frequent appearance of words such as \u0026ldquo;convenient\u0026rdquo; further indicates that advancements in transportation and Socioeconomic development have provided tourists with superior travel experiences and more accessible transit conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 High-Frequency Word Categories\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceptual Dimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-Frequency Words (2009\u0026ndash;2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-Frequency Words (2014\u0026ndash;2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-Frequency Words (2019\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTourism Resources\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMacau (1273) Plazas (887) Churches (558) Architectural Heritage (506) Casinos (496) Heritage Sites (437) Locations (286) Portuguese (274) The Catholic Church (219) Beautiful (218) Aesthetic Appeal (143) Museums (136) Towers (134) History (103) European Style (102) Attractions (99) Appearance (97) Spacious (96) Parks (91) Hong Kong (90) Beautiful (86) Local Culture (84) Architecture (79) Cathedrals (68) Scenic Views (67) Cultural Elements (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHong Kong (3936) Macau (1908) Tourism (1784) Plazas (1556) Architecture (1207) Locations (1170) Churches (878) Beautiful (868) Nightscapes (786) Parks (762) Casinos (758) Skyscrapers (718) Heritage Sites (704) Attractions (509) History (488) Scenic Views (484) St. Paul\u0026rsquo;s Ruins (477) Disneyland (460) The Catholic Church (460) Victoria Harbour (453) Famous Sites (440) Portuguese (395) Aesthetic Appeal (373) Museums (350) Towers (285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHong Kong (1357) Macau (669) Tourism (557) Plazas (445) Architectural Clusters (415) Landmarks (342) Heritage Sites (314) Parks (255) Churches (242) Nightscapes (210) Attractions (191) Scenic Views (188) Beautiful (182) Famous (177) Casinos (160) History (158) Disneyland (155) The Catholic Church (142) Aesthetic Appeal (102) Peninsula Areas (93) Travel Experiences (91) Temples (81) Skyscrapers (80) Museums (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eActivity Experiences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTourism (453) Ambiance (254) Photography (211) Impressions (176) Visits (104) Exhibitions (100) Filming (95) Positive Feedback (76) Tour Concerts (74) Bungee Jumping (72) Future Prospects (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImpressions (799) Ambiance (766) Photography (765) Amusement Facilities (573) Performances (560) Exhibitions (493) Visit (403) Celebrity (379) Expectations (354) Souvenirs (330) Filming (338) Shortcomings (328) Tour Concerts (274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmbiance (766) Photography (765) Amusement Facilities (573) Impressions (170) Visit (150) Exhibitions (132) Performances (129) Celebrity (128) Expectations (115) Popularity (102) Filming (99) Walking Tours (89) Film-Themed Experiences (84) Tour Concerts (77) Hiking (79) Bars (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eService Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHotels (193) Free (139) Cream (78) Restaurants (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHotels (550) Free (564) Slightly (563) Restaurant (387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSlightly (205) Free (163) Hotels (168) Restaurants (114) Souvenirs (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTransportation Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBuses\u0026nbsp;(141)\u0026nbsp;Taxis (113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBuses (960) Ferries (492) Peak Hour Congestion (377) Escalators (320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBuses (284) Ferries (149) Peak Hour Congestion (145) Escalators (128) Entrance(83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStairs (153) Spacious (96) Interior (96) Centers (94) Flagging (87) Fountains (84) Surroundings (72) Staleness (72) Impressions (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStairs (421) Centers (316) Pick-up Points (302) Broadness (321) Districts (368) Middle (289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMany (427) Skyscrapers (200) Districts (196) Centers (128) Stairs (106) Broadness (95) Middle (86) Facilities (82) Local (75) Impressions (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiscellaneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiscellaneous Global (463) Good (290) Many (255) China (214) High (124) Famous (109) Slightly (97) Worth (86) Japan (83) Single (72) Positive (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood (1043) Global (878) Japan (669) China (536) High (510) Joyful (444) Utilize (394) Chaos (341) Tokyo (276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal (372) Good (247) Japan (187) Joyful (178) China (150) High (149) Portugal (88) Duration (85) Utilize (105) Tokyo (74) Convenience (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3.3 Core-Periphery Model\u003c/p\u003e\n\u003cp\u003eTo analyze the intrinsic connections between high-frequency words, subsequent analysis is conducted employing Social Network Analysis (SNA). The specific procedure involved constructing a co-occurrence matrix of high-frequency words using KH Coder, assigning values to the matrix, converting it into a binary matrix, and subsequently conducting core-periphery analysis via Ucinet software. Results revealed Japanese tourists\u0026apos; perceptions of the destination image of Hong Kong and Macau evolved across different time periods, with distinct core and peripheral elements. In the first phase, the core element was tourism resources, while peripheral elements included activity experiences, service quality, transportation facilities, and urban development level. In the second and third phases, tourism resources and activity experiences constituted the core elements, whereas service quality, transportation facilities, and urban development remained as peripheral elements.\u003c/p\u003e\n\u003cp\u003eTourism resources and activity experience serve as the primary drivers influencing tourists\u0026apos; perceptions. Tourism resources maintained core status across all phases,valued not only as tangible entities but also for their symbolic cultural significance. For instance, tourism resources such as the European-style architectural clusters in Macau\u0026rsquo;s historic districts (e.g., the Ruins of St. Paul\u0026rsquo;s), Hong Kong\u0026rsquo;s neon signage culture (e.g., Chungking Mansions), and festival rituals (e.g., the Macau International Fireworks Festival) utilize immersive scene design and symbolic encoding to transform natural landscapes, historical relics, and modern urban sceneries into perceptible cultural images, thereby acting as vessels of emotional memory for tourists. In the early stage, tourists\u0026rsquo; perceptions predominantly focused on the tangible presentation of these resources, resulting in a unidirectional, sightseeing-oriented experience. Over time, however, tourism resources underwent multidimensional narrative reconstruction, gradually evolving into media that trigger emotional responses and interact deeply with activity experiences. The core status of tourism resources is perpetuated not only by their enduring attractiveness but also by their irreplaceability as the cultural nucleus of the destination.\u003c/p\u003e\n\u003cp\u003eIn the second and third phases, activity experience gradually ascended from a peripheral element to a core driving force, with its importance markedly increasing in line with evolving tourist demands. Through mechanisms such as festival rituals and thematic tours, activity experience dynamically activates static tourism resources, effecting a transformation from mere \u0026ldquo;physical presence\u0026rdquo; to \u0026ldquo;emotional memory.\u0026rdquo; During participation, tourists not only achieve sensory satisfaction but also internalize their experiences through individual narrative frameworks, fostering cultural resonance and identity affirmation. The emergence of activity experience signifies a shift in tourists\u0026rsquo; cognitive paradigms\u0026mdash;from traditional \u0026ldquo;scenic viewing\u0026rdquo; to immersive \u0026ldquo;participatory experiences\u0026rdquo;\u0026mdash;thereby serving as the central link connecting tourism resources with tourists\u0026rsquo; emotions. Its diversity and interactivity inject new vitality into the overall destination image.\u003c/p\u003e\n\u003cp\u003eWhile persistently occupying a peripheral position service quality plays a crucial supporting role as the guarantor of a smooth and comfortable experience. Enhancements in standardization and professionalism indirectly elevate the perceived value of tourism resources among tourists. Its weak linkage with activity experience suggests that service quality primarily as a threshold constraint on overall experience quality rather than as a dominant factor. In high-density urban environments, the efficiency and personalization of service quality directly influence tourist satisfaction, forming a vital component of the resilient foundation of destination image.\u003c/p\u003e\n\u003cp\u003eAs peripheral elements, transportation facilities play a core role in optimizing tourists\u0026apos; mobility experiences and resource accessibility. Through the space-time compression effect, these facilities shortening physical distances between tourists and tourism resources, thereby enhancing the continuity and efficiency of the travel experience. The strong association between transportation facilities and tourism resources reflects a symbiotic relationship: optimizing the transportation network boosts the utilization of tourism resources, while the resources agglomeration, in turn, drives upgrades in transportation infrastructure. In high-density urban environments, the topological structure of transportation facilities directly impacts tourists\u0026apos; mobility, constituting an essential component of the overall support system.\u003c/p\u003e\n\u003cp\u003eUrban infrastructure, as the foundational environment, has consistently been a peripheral element; however, its importance has gradually become more pronounced as tourists\u0026apos; demands for environmental quality increase. By integrating modern construction with historical characteristics, urban infrastructure shapes the unique identity of the destination. Its functional adaptability ensures the accessibility of tourism resources and the integrity of the overall experience, making it a vital component of the resilient foundation of the destination\u0026apos;s image. In high-density urban environments, the incorporation of intelligent and miniaturized designs further optimizes the overall tourist experience. Through the integration of technology and services, an innovative pathway\u0026mdash;characterized by the \u0026quot;softening of hard infrastructure\u0026quot;\u0026mdash;has been established.\u003c/p\u003e\n\u003cp\u003eEach influencing factor within the destination image cognition system exhibits a hierarchical functional differentiation. Tourism resources and activity experiences form the core driving layer, shaping deep tourist cognition through the dynamic activation of cultural symbols and the transformation of emotional memories. In contrast, service quality, transportation facilities, and urban infrastructure comprise the peripheral system, sustaining the overall fluidity and stability of the experience through functional adaptation and support. This hierarchical structure reflects the transformation of tourist cognition from static observation to dynamic participation, and from unidirectional projection to multidimensional interaction. The synergistic interplay among these elements across different levels collectively constructs a comprehensive cognitive network of the destination image.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.4 Interaction Mechanisms\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 3, this research qualified the interaction relationships and frequencies among the five influencing factors through a three-stage co-occurrence network analysis. The key findings as follows: Tourism Resources \u0026ndash; Activity Experiences (12), Tourism Resources \u0026ndash; Urban Infrastructure (12), Tourism Resources \u0026ndash; Transportation Facilities (8), Tourism Resources \u0026ndash; Service Quality (6), Service Quality \u0026ndash; Activity Experiences (3), Service Quality \u0026ndash; Transportation Facilities (3), Urban Infrastructure \u0026ndash; Activity Experiences (2), Urban Infrastructure \u0026ndash; Service Quality (1), and Transportation Facilities \u0026ndash; Activity Experiences (1).\u003c/p\u003e\n\u003cp\u003eFrom Figure 3, observing that the synergy between tourism resources, activity experiences, and infrastructure constitutes the core driving force of destination competitiveness. As the material carrier of destination image, tourism resources undergo immersive scene design and symbolic encoding, engaging in multi-layered interactions with activity experiences. Historical architecture clusters, religious sites, and modern urban landscapes are dynamically activated via experiential formats such as festival rituals, artistic performances, and evoking emotional memories and achieving the transformation of visitor perceptions from \u0026ldquo;physical presence \u0026rarr; cultural symbols \u0026rarr; emotional memory.\u0026rdquo;Tourists\u0026rsquo; participatory behaviors (e.g., ritual interactions and consumption practices) integrate static resources into personal narrative frameworks, triggering a bidirectional reinforcement of identity recognition and cultural resonance. Infrastructure, through functional adaptation (such as intelligent navigation systems and vertical spatial development), ensures the accessibility of resources and the integrity of the overall experience. By seamlessly connecting transit hubs with core attractions via metro stations, the conventional disjointed process of arrival and visitation is transformed into a continuous flow of experience. Moreover, Macau\u0026rsquo;s casino-hotel clusters employ a unique \u0026ldquo;organically integrated\u0026rdquo; infrastructure model, where accommodation, dining, and entertainment facilities construct a closed loop to inherently fulfill physiological needs (e.g., dining and restroom facilities), while channeling tourists into the immersive tourism scenarios. Under the high-density urban conditions of Hong Kong and Macau, land constraints amplify the intensity of hierarchical effects. This is manifested in the \u0026ldquo;micro-facility networks supporting high-frequency activation of cultural heritage\u0026rdquo; and \u0026ldquo;seamless transitions between day and night economic scenarios,\u0026rdquo; which together establish emotional anchors and a resilient foundation for the destination branding image.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Figure 3, the complementary relationship among tourism resources, transportation infrastructure, and service quality provides critical support tourist perception. Transportation infrastructure enhance the utilization of tourism resources by minimizing spatial and temporal distances, while the high-density distribution of resources conversely drives the optimization of transportation routes. This interconnection underscores the pivotal role of \u0026quot;mobility convenience\u0026quot; in realizing resource value. Service quality indirectly enhances resource value by improving the comfort of the overall experience, while the complexity of distinctive tourism resources necessitates a higher degree of service quality specialization. This asymmetric relationship indicates that service quality functions primarily as an amplifier of resource value. Moreover, the cross-border location and colonial heritage of Hong Kong and Macau further reinforce the unique efficacy of this tier: Transportation infrastructure operates as convergence hubs for regional synergy, while service quality must perform a bidirectional translation between Eastern and Western cultural symbols and the cultural characteristics of the source market.\u003c/p\u003e\n\u003cp\u003eBased on the findings presented in Figure 3, although the weak connections among service quality, transportation infrastructure, and activity experiences are not statistically significant, they hold important long-tail moderating value. The degree of standardization in service quality directly influences the smoothness of tourists\u0026rsquo; experiences, while the capacity for personalized service\u0026mdash;by addressing the unique demands of visitors\u0026mdash;stimulates innovations in deeper experiential engagement. Information services at transportation nodes optimize tourists\u0026rsquo; mobility experiences by providing real-time, accurate transit information, thereby mitigating friction from information asymmetry. In addition, emergency services in response to transportation disruptions alleviate tourists\u0026rsquo; negative sentiments through timely and effective measures, which in turn positively impact overall perception. Meanwhile, advancements in infrastructure intelligence\u0026mdash;by partially substituting manual services\u0026mdash;not only enhance operational efficiency but also, through the integration of technology and service, create a synergistic enhancement pathway characterized by the \u0026quot;hard infrastructure softness\u0026quot;, further optimizing the comprehensive tourist experience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe figures in Figure 3 imply that through co-occurrence network analysis, the interaction strengths among the five major factors exhibit a pronounced hierarchical differentiation. The synergistic effects between tourism resources and activity experience, as well as between tourism resources and infrastructure (each with a frequency of 12), constitute the core driving layer. Through the dynamic activation of cultural symbols and functional adaptation, this core layer shapes the emotional depth and resilient foundation of the destination image. In contrast, the complementary interactions between tourism resources and transportation facilities (frequency of 8) and between tourism resources and service quality (frequency of 6) form the supporting layer, which leverages spatiotemporal compression effects and and cognitive interventions to optimize resource accessibility and enhance cultural transmission. Additionally, the complementary relationships among service quality, transportation facilities, and activity experiences, operating at low frequencies (\u0026le;3), comprise the long-tail moderating layer. This layer extracts implicit experiential value through the design of emotional touchpoints and embodied technological approaches. In the unique context of Hong Kong and Macau, characterized by high-density and cross-cultural dynamics, these high-frequency mechanisms are further reinforced by spatial constraints and cultural hybridity. This is manifested in the efficient empowerment of micro-infrastructure networks, the seamless transition between day and night scenarios, and the leverage effect of cross-border collaboration, collectively forming a dynamic operational system of \u0026quot;strong core \u0026ndash; elastic periphery.\u0026quot;\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e4.1 Research Findings\u003c/p\u003e\n\u003cp\u003eThis study systematically investigated Japanese tourists\u0026rsquo; perceptual characteristics and dynamic shifts in destination image toward China\u0026rsquo;s Hong Kong and Macau through a multidisciplinary methodology integrating sentiment analysis,high-frequency word analysis, co-occurrence network analysis, and the core-periphery model. Key findings are as follows:(1) High-frequency keyword analysis reveals that Japanese tourists\u0026rsquo; perceptions of the destination image in Hong Kong and Macau have diversified, shifting from a singular cultural sightseeing toward a composite cultural-entertainment experience. (2) Tourism resources and activity experiences constitute the core determinants shaping tourist perceptions. While service quality, transportation infrastructure, and urban development, occupy peripheral positions, they play a significant role in enhancing overall tourist satisfaction. (3) The synergistic effect between tourism resources and activity experiences acts as the primary driving force, whereas service quality, transportation infrastructure, and urban development optimize the overall tourist experience through indirect effects. Together, they form a dynamic system of \u0026quot;strong core \u0026ndash; elastic periphery,\u0026quot; which provides crucial support for improving tourist satisfaction.\u003c/p\u003e\n\u003cp\u003e4.2 Strengths and Limitations\u003c/p\u003e\n\u003cp\u003eThe strengths of this research lie in the integrated application of diverse analytical methods, which significantly enhances its depth. A co-occurrence network was constructed for semantic association analysis, followed by the use of social network analysis to reveal the interaction characteristics among review subjects, thereby the core-periphery structure model was employed to identify the determinants within the overall network, resulting in a comprehensive and in-depth analysis of Japanese tourists\u0026rsquo; review data. By analyzing review data across different time periods, the research reveals the dynamic evolution of Japanese tourists\u0026rsquo; perceptual characteristics, thereby providing robust data support for the long-term planning and strategic adjustments of tourism \u0026nbsp;destinations.\u003c/p\u003e\n\u003cp\u003eThe research has certain limitations: While the study considers the convenience for Japanese tourists traveling to China and acknowledges the strategic importance of Japanese tourists within China\u0026apos;s source markets, Hong Kong and Macau\u0026rsquo;s tourism ecosystems cater to a diverse array of international visitors. Thus, the research based solely on the perspective of Japanese tourists to assess the overall destination image perception of Hong Kong and Macau presents certain limitations. Consequently, future research could further explore the differences in destination image perceptions among tourists from various source markets, thereby advancing our understanding of the overall tourism image characteristics of Hong Kong and Macau.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.3 Policy implications\u003c/p\u003e\n\u003cp\u003eFirstly, strengthen protection of existing tourism resources, particularly important cultural heritage sites such as Macau\u0026apos;s historic Centre, to ensure the continuity of their historical and cultural value. Simultaneously, more tourism projects with rich cultural connotations should be developed by integrating cultural, natural, and entertainment resources with diversified tourism packages designation that cater to the needs of various tourist segments. Additionally, the expansion of cultural festivals and interactive experience programs is recommended. By leveraging technologies such as virtual reality (VR) and augmented reality (AR), tourism activities can be made more engaging and interactive, ultimately enhancing the immersive and experiential quality for tourists.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecondly, implement systematic training programs within the tourism service industry to elevate overall service quality\u0026mdash;particularly in hotels and restaurants\u0026mdash;in order to provide more personalized and premium services. Concurrently, establish a comprehensive tourism service quality evaluation system that facilitates the timely collection of visitor feedback and supports continuous improvement. Furthermore, regularly conduct tourist satisfaction surveys to identify service deficiencies and implement targeted measures to address these gaps.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, increase the frequency and coverage of public transportation to optimize network layout and reduce travel time for tourists. Strengthen traffic management, particularly during peak tourism seasons, to mitigate congestion and enhance the overall travel experience. Leverage intelligent traffic management systems and real-time traffic information dissemination to guide tourists in planning their trips efficiently. Additionally, utilize mobile applications to provide real-time traffic updates and navigation services, ensuring a seamless and convenient travel experience.\u003c/p\u003e\n\u003cp\u003eFinally, enhance the environmental quality of urban public spaces by expanding green infrastructure to improve the aesthetic appeal and overall comfort of the city. Promote the urban cultural-tourism synergistic development, transforming cities into culturally distinctive tourist destinations. Strengthen the preservation and development of cultural activities and historical sites to deepen the city\u0026rsquo;s cultural capital and enhance its attractiveness to visitors.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research employs a comprehensive methodological framework that integrates sentiment analysis, high-frequency word analysis, co-occurrence network analysis, and the core-periphery structural analysis to explore the perceptual characteristics and dynamic evolution of Japanese tourists\u0026rsquo; destination image of Hong Kong and Macao. The key conclusions are as follows:(1) Japanese tourists predominantly express positive perceptions toward Hong Kong and Macao, particularly appreciating natural landscapes, historical and cultural heritage, and mobility convenience. However, negative emotions are observed regarding issues such as insufficient tourist facilities, service inconsistencies, and transportation inefficiencies. (2) The focus of Japanese tourists has progressively transitioned from an emphasis on historical and cultural heritage to modern urban landscapes and immersive entertainment experiences. This shift reflects a movement from traditional, static cultural sightseeing towards dynamic, experience-driven tourism consumption. (3) Tourism resources and activity experiences constitute the core components shaping Japanese tourists\u0026rsquo; perception of Hong Kong and Macao\u0026rsquo;s destination image, which always occupy a dominant position. In contrast, elements such as service quality, transportation infrastructure, and urban development, while peripheral, play a significant role in enhancing visitor satisfaction, particularly by improving accessibility and modernizing urban facilities. (4) The dynamic interaction between tourism resources and activity experiences serves as the primary driving force in shaping destination perception. Meanwhile, service quality, transportation infrastructure, and urban development act as supporting factors that indirectly enhance the overall visitor experience. Collectively, these elements form a \u0026quot;strong core\u0026mdash;elastic periphery\u0026quot; dynamic interaction system, which plays a vital role in elevating tourist satisfaction.(5) This research pioneers the integration of core-periphery modeling with user-generated content (UGC) analytics, proposing a dual-driven framework of \u0026quot;cultural symbol activation\u0026mdash;technological infrastructure empowerment.\u0026quot; This approach offers an innovative methodology for managing tourism destination images within high-density urban environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Natural Science Foundation of Ningbo,grant number 20221JCGY010743 and Shaanxi Provincial Philosophy and Social Science Research Special Program, grant number 2025YB0213\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Availability Statement\u003c/b\u003e: The data can be made available upon request to the corresponding author. These data are not publicly available.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthics Statement\u003c/b\u003e: Not applicable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflict of Interest Statement\u003c/b\u003e: The authors declare that they have no known financial or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.G. and W.K.S. wrote the original draft. M.M.S. contributed methodology, software, and validation. Y.W.W. and Y.Z. handled writing - review and editing. Y.J. conducted investigation, writing - review and editing, and obtained funding. W.T.Y. was responsible for conceptualization, writing - original draft, and writing - review and editing as the corresponding author. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLevy, S.J. Marketplace Behavior- Its Meaning for Management. New York: Amacom，1978\u003c/li\u003e\n\u003cli\u003eCrompton, J.L. Motivations for pleasure vacation. Annals of Tourism Research,1979,6(4), 408-424.\u003c/li\u003e\n\u003cli\u003eFakeye, P.C., \u0026amp; Crompton, J.L.. Image differences between prospective, first-time and repeat visitors to the lower Rio Grande Valley.Journal of Travel Research, 1991,30(2), 10-16.\u003c/li\u003e\n\u003cli\u003eBaloglu, S., \u0026amp; McCleary, K.W.A model of destination image formation. Annals of Tourism Research,1999b,26(4), 868-897.\u003c/li\u003e\n\u003cli\u003eHunt J D. Image--A Factor in Tourism[M]. Colorado State University, 1971.\u003c/li\u003e\n\u003cli\u003eStepchenkova, S., \u0026amp; Mills, E.J. Destination image: A meta-analysis of 2000-2007 research. Journal of Marketing \u0026amp; Management,2010,19(6), 575-609.\u003c/li\u003e\n\u003cli\u003eGallarza, M.G., Saura, I.G., \u0026amp; Garc\u0026iacute;a, H.C. Destination image: Towards a conceptual framework. Annals of Tourism Research, 2002,29(1), 56-78.\u003c/li\u003e\n\u003cli\u003e. Miao, L., \u0026amp; Wei, W. Consumers\u0026rsquo; pro-environmental behavior and the underlying motivations: A comparison between household and hotel settings. International Journal of Hospitality Management 32, 2013,102-112.\u003c/li\u003e\n\u003cli\u003eBuhalis, D. Marketing the competitive destination of the future. Tourism Management,2000,21(1), 97-116.\u003c/li\u003e\n\u003cli\u003eChen, C.F., \u0026amp; Tsai, D.C. How destination image and evaluative factors affect behavioural intentions?Tourism Management, 2007, 28(4), 1115-1122.\u003c/li\u003e\n\u003cli\u003eBoulding K E. The image: Knowledge in life and society[M]. University of Michigan press, 1961.\u003c/li\u003e\n\u003cli\u003eCamis\u0026oacute;n C. Total quality management in hospitality: an application of the EFQM model[J]. Tourism management, 1996, 17(3): 191-201.\u003c/li\u003e\n\u003cli\u003eMaital S. \u0026quot; The Experience Economy: Work Is Theatre \u0026amp; Every Business a Stage\u0026quot; by B. Joseph Pine II and James H. Gilmore (Book Review)[J]. Sloan Management Review, 1999, 40(4): 93.\u003c/li\u003e\n\u003cli\u003eAli F, Kim W G, Li J, et al. Make it delightful: Customers\u0026apos; experience, satisfaction and loyalty in Malaysian theme parks[J]. Journal of destination marketing \u0026amp; management, 2018, 7: 1-11.\u003c/li\u003e\n\u003cli\u003eCrompton J L. Motivations for pleasure vacation[J]. Annals of tourism research, 1979, 6(4): 408-424.\u003c/li\u003e\n\u003cli\u003eGartner WC， Image Formation Process. J. Travel Tour. Mark. 2(2/3): 1993,191-215.\u003c/li\u003e\n\u003cli\u003ePizam A, Sussmann S. Does nationality affect tourist behavior?[J]. Annals of tourism research, 1995, 22(4): 901-917.\u003c/li\u003e\n\u003cli\u003eAvraham E. Destination marketing and image repair during tourism crises: The case of Egypt[J]. Journal of Hospitality and Tourism Management, 2016, 28: 41-48.\u003c/li\u003e\n\u003cli\u003eRitchie B W, Jiang Y. A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management[J]. Annals of tourism Research, 2019, 79: 102812.\u003c/li\u003e\n\u003cli\u003eLine N D, Hanks L. The effects of environmental and luxury beliefs on intention to patronize green hotels: The moderating effect of destination image[J]. Journal of Sustainable Tourism, 2016, 24(6): 904-925.\u003c/li\u003e\n\u003cli\u003eBudeanu A, Miller G, Moscardo G, et al. Sustainable tourism, progress, challenges and opportunities: an introduction[J]. Journal of cleaner production, 2016, 111: 285-294.\u003c/li\u003e\n\u003cli\u003ePike S, Page S J. Destination Marketing Organizations and destination marketing: A narrative analysis of the literature[J]. Tourism management, 2014, 41: 202-227.\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;zdemir C, Yolal M. Cross-cultural tourist behavior: An examination of tourists\u0026rsquo; behavior in guided tours[J]. Tourism and Hospitality Research, 2017, 17(3): 314-324.\u003c/li\u003e\n\u003cli\u003eLee Y K, Pei F, Ryu K, et al. Why the tripartite relationship of place attachment, loyalty, and pro-environmental behaviour matter?[J]. Asia Pacific Journal of Tourism Research, 2019, 24(3): 250-267.\u003c/li\u003e\n\u003cli\u003eWang R, Hao J X, Law R, et al. Examining destination images from travel blogs: A big data analytical approach using latent Dirichlet allocation[J]. Asia Pacific Journal of Tourism Research, 2019, 24(11): 1092-1107.\u003c/li\u003e\n\u003cli\u003ePark D, Lee G, Kim W G, et al. Social network analysis as a valuable tool for understanding tourists\u0026rsquo; multi-attraction travel behavioral intention to revisit and recommend[J]. Sustainability, 2019, 11(9): 2497.\u003c/li\u003e\n\u003cli\u003eRen G, Hong T. Investigating online destination images using a topic-based sentiment analysis approach[J]. Sustainability, 2017, 9(10): 1765.\u003c/li\u003e\n\u003cli\u003eMarine-Roig E. Destination image analytics through traveller-generated content[J]. Sustainability, 2019, 11(12): 3392.\u003c/li\u003e\n\u003cli\u003eZhang Y, Gao J, Cole S, et al. How the spread of user-generated contents (UGC) shapes international tourism distribution: Using agent-based modeling to inform strategic UGC marketing[J]. Journal of Travel Research, 2021, 60(7): 1469-1491.\u003c/li\u003e\n\u003cli\u003eYamagishi K, Canayong D, Domingo M, et al. User-generated content on Gen Z tourist visit intention: a stimulus-organism-response approach[J]. Journal of Hospitality and Tourism Insights, 2024, 7(4): 1949-1973.\u003c/li\u003e\n\u003cli\u003eKirilenko A P, Stepchenkova S O, Hernandez J M. Comparative clustering of destination attractions for different origin markets with network and spatial analyses of online reviews[J]. Tourism management, 2019, 72: 400-410.\u003c/li\u003e\n\u003cli\u003eWong, C. U. I., \u0026amp; Qi, S. Tracking the evolution of a destination\u0026rsquo;s image by text-mining online reviews - the case of Macau. Tourism Management Perspectives,2017,23, 19\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eJimenez-Barreto J, Sthapit E, Rubio N, et al. Exploring the dimensions of online destination brand experience: Spanish and North American tourists\u0026apos; perspectives[J]. Tourism Management Perspectives, 2019, 31: 348-360.\u003c/li\u003e\n\u003cli\u003eLian, T., \u0026amp; Yu, C. Representation of online image of tourist destination: A content analysis of Huangshan. Asia Pacific Journal of Tourism Research,2017, 22(10), 1063\u0026ndash;1082.\u003c/li\u003e\n\u003cli\u003eMak, A. H. N.Online destination image: Comparing national tourism organisation\u0026rsquo;s and tourists\u0026rsquo; perspectives.Tourism Management,2017, 60, 280\u0026ndash;297\u003c/li\u003e\n\u003cli\u003eLam, J. M., Ismail, H., \u0026amp; Lee, S.From desktop to destination: User-generated content platforms, co-created online experiences, destination image and satisfaction. Journal of Destination Marketing \u0026amp; Management,2020, 18, 100490.\u003c/li\u003e\n\u003cli\u003eChoi, S., Lehto, X. Y., \u0026amp; Morrison, A. M. Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management,2007, 28(1), 118\u0026ndash;129.\u003c/li\u003e\n\u003cli\u003eXiang, Z.Role of social media in online travel information search. Tourism Management,2010, 31(2), 179\u0026ndash;188.\u003c/li\u003e\n\u003cli\u003eBaloglu, S., \u0026amp; Brinberg, D. Affective images of tourism destinations. Journal of Travel Research,1997, 35(4), 11\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eWang, C., \u0026amp; Hsu, M. The relationships of destination image, satisfaction, and Behavioral Intentions: An integratedmodel. Journal of Travel and Tourism Marketing, 201027(8), 829\u0026ndash;843.\u003c/li\u003e\n\u003cli\u003eSelin S, Chavez D. Develo an evolutionary tourism partnership model[J]. Annals of tourism research, 1995, 22(4): 844-856.\u003c/li\u003e\n\u003cli\u003eMcKercher B, Shoval N, Ng E, et al. Using GPS data to compare first-time and repeat visitors to Hong Kong[J]. Tourism Geographies, 2012, 14(1): 147-161.\u003c/li\u003e\n\u003cli\u003eDredge D. Policy networks and the local organisation of tourism[J]. Tourism management, 2006, 27(2): 269-280.\u003c/li\u003e\n\u003cli\u003eLi Y, Chen Y, Fan Y, et al. Dynamic network modeling of gut microbiota during Alzheimer\u0026rsquo;s disease progression in mice[J]. Gut Microbes, 2023, 15(1): 2172672.\u003c/li\u003e\n\u003cli\u003ePark D, Lee G, Kim W G, et al. Social network analysis as a valuable tool for understanding tourists\u0026rsquo; multi-attraction travel behavioral intention to revisit and recommend[J]. Sustainability, 2019, 11(9): 2497.\u003c/li\u003e\n\u003cli\u003eRitchie B W, Jiang Y. A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management[J]. Annals of tourism Research, 2019, 79: 102812.\u003c/li\u003e\n\u003cli\u003eLi M, Wang X, Gao K, et al. A survey on information diffusion in online social networks: Models and methods[J]. Information, 2017, 8(4): 118.\u003c/li\u003e\n\u003cli\u003eПанг Б, Ли Л. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval[J]. 2008.\u003c/li\u003e\n\u003cli\u003e樋口耕一. A two-step approach to quantitative content analysis: KH Coder tutorial using Anne of Green Gables (Part I)[J]. 立命館産業社会論集= 立命館産業社会論集, 2016, 52(3): 77-91.\u003c/li\u003e\n\u003cli\u003eMarine-Roig E, Clav\u0026eacute; S A. Tourism analytics with massive user-generated content: A case study of Barcelona[J]. Journal of Destination Marketing \u0026amp; Management, 2015, 4(3): 162-172.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Destination Image Perception, Japanese Tourists, Hong Kong and Macau Regions, Core-Periphery structure Model","lastPublishedDoi":"10.21203/rs.3.rs-6637197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6637197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgainst the backdrop of intensified global tourism competition, the perception of destination image has become increasingly influential in tourist decision-making. However, existing research has lacked in-depth exploration of the image perception mechanisms of Japanese tourists in the Hong Kong and Macau regions, particularly the dynamic analysis based on user-generated content (UGC) remains underexplored. Drawing upon online reviews authored by Japanese tourists between 2009 and 2023, this research applies sentiment analysis, high-frequency lexical analysis, and a core\u0026ndash;periphery modeling approach to uncover the defining characteristics of their perceptions and their underlying mechanisms. The results indicate that: (1) Japanese tourists predominantly exhibit positive sentiments toward Hong Kong and Macao, primarily driven by natural scenery, historical and cultural attractions, and transportation infrastructure, yet express negative feedback regarding issues such as attraction facilities and service quality. (2) The evolution of the tourists\u0026rsquo; destination image perceptions reflects a diversified trend, shifting from singular cultural sightseeing to composite culture-entertainment experiences, with modernized resources and interactive activities emerging as new satisfaction drivers. (3) Tourism resources and activity experiences constitute the core elements of tourist perception, while service quality, transportation facilities, and urban development occupy peripheral positions that play a supportive role in enhancing tourist satisfaction. (4) The synergistic effect between tourism resources and activity experiences exerts the strongest influence, with peripheral factors\u0026mdash;service quality, transport infrastructure, and urban growth\u0026mdash;indirectly enhancing the central experiential components.\u003c/p\u003e","manuscriptTitle":"Perceptions of Destination Image among Japanese Tourists toward the Hong Kong and Macau Regions of China: An Analysis Based on Online Reviews","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 06:50:38","doi":"10.21203/rs.3.rs-6637197/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1832e18a-6939-4e2b-a114-dd3077cb0e09","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49504138,"name":"Humanities/Language and linguistics"},{"id":49504139,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-09-06T17:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 06:50:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6637197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6637197","identity":"rs-6637197","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}