Digital culture and tourism: Perception of landscape culture and distribution of homestay

Chen, Yuegang; Pan, Yuting; Chen, Yuxin; Wu, Yan Article Digital culture and tourism: Perception of landscape culture and distribution of homestay Amfiteatru Economic Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Chen, Yuegang; Pan, Yuting; Chen, Yuxin; Wu, Yan (2025) : Digital culture and tourism: Perception of landscape culture and distribution of homestay, Amfiteatru Economic, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 27, Iss. 70, pp. 1172-1191, https://doi.org/10.24818/EA/2025/70/1172 This Version is available at: https://hdl.handle.net/10419/328041 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1172 Amfiteatru Economic DIGITAL CULTURE AND TOURISM: PERCEPTION OF LANDSCAPE CULTURE AND DISTRIBUTION OF HOMESTAY Yuegang Chen1 * , Yuting Pan2, Yuxin Chen3 and Yan Wu4 1)2)3)SILC Business School, Shanghai University, Shanghai, China 4)International Economics and Trade School, Shanghai Lixin University of Accounting and Finance, Shanghai, China Please cite this article as: Chen, Y., Pan, Y., Chen, Y. and Wu, Y., 2025. Digital Culture and Tourism: Perception of Landscape Culture and Distribution of Homestay. Amfiteatru Economic, 26(67), pp. 1172-1191. DOI: https://doi.org/10.24818/EA/2025/70/1172 Article History Received: 19 March 2025 Revised: 7 May 2025 Accepted: 16 June 2025 Abstract This research explores how landscape cultural perception impacts the spatial distribution of homestay businesses, and highlights the increasing role of digital technology in enhancing cultural tourism experiences and optimizing homestay industry operations. Using grounded theory, the research gets a relationship matrix to explore the theoretical logic between landscape resources and cultural perceptions, and then based on the S-O-R model, a theoretical framework is constructed to explain the impact of landscape culture perception on the distribution of homestays. The research also applies an empirical approach to analyse how cultural perceptions of landscape play in shaping the homestay distribution in Yangtze River Delta China. Additionally, digital platforms are found to enhance tourists’ cultural perceptions and streamline their booking experiences, while providing homestay operators with effective tools for personalised marketing and operational efficiency. Keywords: digital culture and tourism, spatial distribution of homestays, perception of landscape culture, traffic accessibility, digital platform, grounded theory, ridge regression. JEL Classification: R12. Z32. C21 * Corresponding author, Yuegang Chen – e-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Economic Interferences AE Vol. 27 • No. 70 • August 2025 1173 Introduction In recent years, the global tourism market has exhibited robust recovery momentum. For example, Iceland has successfully attracted a significant number of tourists by promoting its unique image as the "Land of Fire and Ice," using its distinctive natural landscapes such as the Northern Lights and volcanic hot springs, which have established it as a worldrenowned tourist destination (Röslmaier & Ioannides, 2023). Similarly, other tourist hotspots, such as the "Snow Festival" in Hokkaido, Japan, and the "Arctic Light Tour" in Finland, have capitalised on their unique cultural and natural resources to captivate global tourists, driving substantial growth in related industries (Nakayama, 2024; Inkinen et al., 2024). Alongside the flourishing tourism sector, supporting industries such as accommodation, dining, and other services have experienced rapid expansion (Ivanov & Webster, 2007; Andereck et al., 2005). Among these, the homestay sector, characterized by its personalized services and strong local cultural elements, has emerged as a significant form of tourism globally. Research has shown that the booming homestay industry can effectively stimulate local economies and promote synergies with related industries (Jamal et al., 2011). In regions rich in cultural and tourism resources, homestay clusters have become one of the most dynamic forms of tourism spatial organization (Guttentag et al., 2017). Globally, multiple cross-regional homestay clusters have emerged, such as Napa Valley in California, Provence in France, and Tuscany in Italy (Jones et al., 2015; Van et al., 1996). These regions have successfully used local cultural resources and unique natural conditions to attract a large numbers of tourists, fostering the development of housing clusters. However, there are still many issues worthy of in-depth exploration. Currently, research on the internal relationship between tourism and homestays, especially the impact mechanism of different landscape cultural perceptions on the distribution of accessibility of homestays and the role of transportation in this process, remains relatively limited. Furthermore, with the widespread application of digital technology in the tourism field, it has had a profound impact on the tourism and homestay industries. Digital platforms not only provide tourists with rich cultural information, helping them better understand the cultural connotations of tourist destinations and enhancing their cultural awareness, but also simplify the booking process, allowing tourists to arrange their trips more conveniently. For homestay operators, digital platforms offer an effective way to personalized marketing. Through technical means such as big-data analysis, they can accurately grasp tourists' needs, develop targeted marketing strategies, and at the same time improve operational efficiency and reduce operating costs. This research has made important contributions in many aspects. First, it is the first to systematically classify and quantify landscape cultural perceptions, construct a brand-new theoretical framework, and provide a systematic and scientific method for understanding the relationship between landscape resources and cultural perceptions. Through empirical research, the association between landscape cultural perceptions and homestay distributions has been revealed, enriching and improving the relevant theoretical system. Second, it provides practical insights for tourism professionals and managers, helping them better understand market demands, rationally plan homestay layouts, improve service quality, and promote the sustainable development of the cultural tourism and homestay industries. AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1174 Amfiteatru Economic 1. Literature review In the fields of tourism geography and economic geography, the spatial distribution relationship between homestays and tourist attractions has always been a major research issue. Past studies have clearly shown that homestays tend to cluster in areas rich in tourism resources, and their distribution is influenced by various factors such as the attractiveness of tourist attractions, accessibility to transportation, and the state of infrastructure. Many scholars have explored the spatial agglomeration effect of tourist attractions and homestays and found that in areas with important natural landscapes, homestays usually cluster around tourist attractions to improve tourists' convenience and experience. Moreover, the distribution of homestays does not solely rely on natural resources; local culture and socialeconomic conditions also have a significant impact on it (Qian et al., 2023). Taking the Yangtze River Delta China as an example, cities rich in cultural and tourism resources such as Hangzhou and Suzhou show a significant positive correlation between the number of homestays and the number of tourist attractions. Furthermore, transportation infrastructure such as roads, airports and railway stations has also been proven to be a key factor in influencing the distribution of homestays and tourist attractions (Zeng et al., 2024; Qian et al., 2023). However, existing studies mostly focus on the spatial relationship between natural attractions and homestays, paying relatively little attention to tourists' cultural perceptions of attractions. The cultural shaping of tourist attractions plays a crucial role in the tourism industry. It not only directly determines the ability of attractions to attract tourists but also affects the distribution and types of nearby homestays (Guttentag et al., 2017; Huan et al., 2023; Wu and Chen, 2023). Many tourist attractions improve tourists' cultural experiences by excavating and displaying local cultural resources, such as historical relics, folk activities, and traditional architectural styles (Jamaludin et al., 2012). Successful cultural shaping often makes the cultural expressions of tourist attractions and homestays echo each other. As an important window for tourists to understand local culture, the cultural connotation of homestays is closely linked to that of attractions (KC and Thapa, 2024). In historical cities and UNESCO World Heritage sites, tourist attractions with profound cultural heritage are more likely to attract personalised high-end homestays (Qiao et al., 2021), and the depth of cultural shaping in these areas is closely related to the development model of homestays. However, there is a lack of research on quantifying tourists' cultural perceptions of attractions and exploring how these perceptions affect the distribution of homestays. With the extensive penetration of digital technology in the tourism industry, the relationship between cultural shaping and the distribution of homestays has undergone profound changes (Li et al., 2024). Digital technology, through online cultural content and booking platforms, has greatly improved tourists' cultural awareness of tourist destinations (Goncalves et al., 2022). Current research shows that functions such as virtual tours and video demonstrations provided by digital tourism platforms can help tourists deeply understand the cultural background of destinations before departure, thus improving their cultural satisfaction after arrival (Chakravarty, 2024; Jia et al., 2024). At the same time, the emergence of online booking platforms such as Airbnb and Booking.com has completely changed the pattern of the homestay market, enabling hosts to use digital tools for precise marketing and service optimisation. Digital technology has also further promoted the interaction between the cultural shaping of tourist attractions and the distribution of homestays (Gupta, 2024). Economic Interferences AE Vol. 27 • No. 70 • August 2025 1175 In summary, first, although there has been a large amount of research on homestay distribution, cultural shaping of tourist attractions and the application of digital technology, the existing literature still has some fragmentation problems. Second, most studies focus on the relationship between homestays and natural tourist attractions, but the research on the interaction mechanism between the culture of tourist attractions and the distribution of homestays is not in-depth enough. Third, existing studies on the impact of digital technology on the tourism industry mostly focus on enhancing tourists' experiences, ignoring its in-depth impacts on perception of landscape cultural and distribution of homestays. 2. Theoretical framework 2.1. The classification of landscape cultural perception Based on perceptions of historical and geographical cultural resources, tourists’ perceptions of landscape culture can be categorized into four types: modern and humanities, ancient and nature, modern and nature, and ancient and humanities, as shown in Table no. 1. Table no. 1. Classification of tourists’ perception of landscape culture Cultural perception classification Geographical perception of cultural resources Historical perception of cultural resources Case Humanities Nature Ancient Modern Geographical perception of cultural resources Humanistic perception Modern Humanities: Modern landscape produced by human influence after 1949. Shanghai Science and Technology Museum, Tianyige Museum, etc. Natural perception Ancient Nature: Ancient natural scenery that existed before 1949. Xuedou Mountain, Suzhou Creek, etc. Historical perception of cultural resources Modern perception Modern Nature: Natural scenery with modern characteristics was developed after 1949. Century Park, Chenshan Botanical Garden, etc. Ancient perception Ancient Humanities: Ancient landscape produced by human influence before 1949. Lu Xun’s hometown scenic spot, Chen Yun’s former residence, etc. AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1176 Amfiteatru Economic 2.2. The measurement of landscape cultural perception Using the core concept of grounded theory and data from Ctrip’s tourism website, along with user comments, Nvivo12 software is employed to construct the theoretical logic of the relationship between tourists’ landscape resources and cultural perceptions, resulting in a relationship matrix that measures the degree of landscape cultural perception. Two types of original texts were used: First, the introduction texts of scenic spots from Ctrip.com. A total of 6,250 introduction texts from 27 cities in the Yangtze River Delta region of China were extracted using Python software, and 5,659 effective samples were obtained after manual review and screening. Second, tourists’ perception comments were collected. Based on the number of effective scenic spots in each city, 540 spots were selected as data for tourists’ perception. Duplicate comments were excluded, and 1,569 valid perception comments were obtained. To ensure data quality, additional comments were collected from Qunar.com and TripAdvisor for triangulation. (1) Coding process Following the grounded technology of procedural coding, this research utilizes Nvivo12 software for open coding, axial coding, and selective coding of scenic spot introductions and tourists’ perception comments. This process of labeling, conceptualizing, and categorizing identifies relationships among several factors and constructs a theoretical model from the bottom up. Open coding. Open coding involves conceptualizing data to form free nodes, preserving the original meaning of the text while eliminating less relevant nodes with fewer than three occurrences and merging those with similar meanings, forming 64 initial categories. Axial coding. Axial coding is the process of inductively categorizing and constantly comparing to connect initial category codes together. This process involves selecting and constructing the content of main categories and linking primary conceptual categories with secondary ones to reorganize the data and establish logical relationships between the categories. The initial categories are synthesized to form 18 sub-categories and 6 main categories. Selective coding. Selective coding, the third stage, extracts the core category and systematically and analyzes its relationship with other categories in order to get the canonical relationship structure of the main categories. By comparing the categories formed through the three codes, the core category “Yangtze River Delta tourism landscape perception” is determined, and tourists’ landscape perceptions are divided into two dimensions: historical resource perception and geographical resource perception. Around the core category, a logical model of the relationship between tourists’ perception of landscape resources and culture is constructed, as shown in Figure no. 1. Economic Interferences AE Vol. 27 • No. 70 • August 2025 1177 Figure no. 1. The theoretical logic relationship between tourists’ landscape resources and cultural perception on digital platform (2) Saturation test To test the theoretical saturation of the model, 20 recent tourist reviews were randomly obtained from Ctrip.com. Additionally, Nvivo12 software was used to search all coded samples. Results indicate that the key elements of tourists’ landscape perception and their logical relationships are encompassed within the core category, and no new primary or secondary categories were identified, indicating the coding is approaching saturation. Four representative texts are provided as evidence. Text 1: Moganshan Scenic Area, located west of Deqing County, Huzhou City, Zhejiang Province, covers an area of about 4 square kilometers. Major scenic spots include Jianchi, Qingliang Pavilion, Xuguangtai, Dicuitan, Dakeng, and Mogan Lake. The area boasts high forest coverage, with more than 180 peaks, the highest being Tashan at 720 meters above sea level. The scenic area also offers summer villas, resorts, and homestays, providing good options for viewing and accommodation during summer. Text 2: The image of Bao Gong’s impartiality is deeply rooted in public consciousness. Since ancient times, Bao Gong has been highly admired. The Bao Gong Temple, built before the Ming Dynasty, was primarily constructed to commemorate Bao Gong. During the Qing Dynasty, Li Hongzhang invested heavily in its renovation. Text 3: I visited the Humble Administrator’s Garden in the afternoon and spent two hours exploring. The scenery is remarkable; it is, after all, one of the four famous gardens in China. I particularly enjoyed its “borrowed scenery” technique. The park features a variety of buildings that harmoniously complement the landscape, making it highly recommended. Renting an interpreter is an option; however, note that the duration should not exceed one hour to avoid high costs. Text 4: The scenic spot is conveniently located near the city, just a short drive away. I bought a package ticket for 110 yuan, which was cost-effective. The route offers views of mountains and rivers, and some sites have very vivid names. The spot also features glass rafting and a sightseeing elevator. Visitors can enjoy continuous performances during the boat ride, adding interest to the experience. AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1178 Amfiteatru Economic (3) Relationship matrix of landscape culture perception Tagging the introduction texts of scenic spots on tourism websites, coding scenic spots, categorizing the cultural perception of scenic spots, and constructing the relationship matrix between landscape resources and cultural perception types were conducted according to the theoretical logic. Finally, 222 landscape resource tags were summarized, with some relationship matrices shown in Table no. 2, where 1 indicates “related” and 0 indicates “unrelated.” Table no. 2. The relationship matrix of cultural perception of tourism landscape Tourism Landscape Location code (top four) Landscape resources Cultural perception of landscape resources Modern humanity Ancient nature Modern nature Ancient humanity Jiuhua Mountain in Chizhou, Anhui Province 2428 “natural scenery” 0 1 1 0 “cultural relics” 0 0 0 1 Shanghai Oriental Pearl 2001 “highaltitude landscape” 1 0 1 0 …… …… …… …… …… …… …… Changzhou Dinosaur Valley Hot Spring, Jiangsu 2131 “soaking in hot springs” 0 1 1 0 Huayi Brothers Film World 2151 “amusement park” 1 0 0 0 2.3. The influence mechanism To explore how tourists' perceptions of landscape culture influence the distribution of homestay businesses more deeply, this research adopts the S-O-R model (StimulusOrganism-Response Model), dividing the process that influences consumer decisionmaking into three main stages: Stage 1: Perception and Information Acquisition. Tourists use online platforms such as Ctrip and Qunar to comprehensively understand the cultural resources of scenic spots and the attributes of homestay products. The abundant information provided by digital platforms helps tourists form perceptions of landscape cultural resources. This cultural perception is influenced not only by the characteristics of the landscape itself but also by the cognitive processes of the tourists. For example, although faux-ancient architecture is modern in construction, it can still evoke tourists' perception of ancient culture; conversely, the use of modern elements can give traditional landscapes a contemporary feel. This process highlights the importance of digital cultural dissemination, as tourists gradually build awareness and interest in destination cultural resources through browsing online information. Economic Interferences AE Vol. 27 • No. 70 • August 2025 1179 Stage 2: Formation and Reinforcement of Motivation. When the cultural resources of a scenic area obtained online align with the tourists' preferences, the stimuli provided by digital platforms further strengthen their motivation. Tourists' prior cognition and emotional interaction are reinforced, and digital cultural content gradually transforms into their motivation to visit the scenic spot. At the same time, tourists can complete online bookings for transportation and homestays via digital platforms, reducing the decision-making cycle. Personalized recommendations further enhance their willingness to travel (Xu and Luo, 2023). Stage 3: Experience and Feedback. Tourists convert their perceptions into real consumption behaviors by physically experiencing the cultural resources of the scenic area and staying at homestays. At this stage, accessibility becomes a critical factor. Scenic spots and homestays with convenient transportation are better able to attract tourists, strengthening the impact of cultural resources on the clustering of homestays. After experiencing the landscape culture and homestay products, tourists often post feedback online, such as ratings or travel blogs. This online feedback not only reflects their consumption experience but also provides valuable information for other potential tourists, forming a new cycle of information dissemination (Bridges and Vásquez, 2018).The experience of tourists visiting attractions affects the site selection decisions of homestay business owners, which in turn influences the distribution of homestays, as illustrated in Figure no. 2. Figure no. 2. The influence mechanism of landscape culture perception on the distribution of homestay 3. Association induction between landscape cultural perception and homestay spatial distribution 3.1. Variables independent variables. The independent variable is the perception of tourism landscape culture, weighted according to ratings by the Ministry of Culture and Tourism. If a scenic spot has a national rating of 5A, it scores 10 points; 4A scores 8 points, and 3A and below scores 2 points. The total score for each zip code area is calculated by multiplying the number of scenic spots at each grade by its weight, then summing these values. Equation (1) shows how to calculate the representation intensity of the four types of cultural resource perception across 154 zip codes. AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1186 Amfiteatru Economic (4) Robust test To further validate the reliability of the conclusions, a robustness test was conducted by substituting the explanatory variables. Two main adjustments were made: first, using a ratio method to calculate the number of scenic spots in the corresponding area to minimize dimensional influence; and second, reducing the impact of scenic spot grades by adjusting the weights assigned to different scenic spot grades. The specific calculation method for the independent variables is shown in Equation (6). (6) In Formula (5), denotes the type of independent variable （=1,2,3,4 ）; is the corresponding postal code area number （=1,2,3…）; represents the grade of the scenic area (5A, 4A, 3A, or below); is the assigned value for the scenic area grade (5 for 5A, 4 for 4A, and 2 for 3A or below); and is the number of scenic areas in the corresponding region. Even after modifying the calculation method for the independent variables, multicollinearity persisted, as shown in Table no. 8. Consequently, the ridge regression method was employed for robustness testing, with results presented in Table no. 9. Table no. 8. Robustness test of linear regression analysis Model 18 Model 19 Model 20 Model 21 Model 22 VIF constant 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) -0.000 (-0.000) 0.000 (0.000) - X1 0.327*** (4.289) -0.342* (-1.752) 7.621 X2 0.351*** (4.687) 0.194* (1.675) 2.675 X3 0.399*** (5.326) 0.490** (1.995) 12.086 X4 0.411*** (5.390) 0.104 (0.518) 8.034 Price 0.148* (1.969) 0.103 (1.370) 0.157** (2.152) 0.132* (1.814) 0.130* (1.765) 1.093 Clean 0.043 (0.446) 0.105 (1.083) 0.027 (0.290) 0.033 (0.349) 0.055 (0.575) 1.839 Ser&Dec 0.131 (1.331) 0.131 (1.342) 0.108 (1.126) 0.083 (0.859) 0.091 (0.948) 1.856 N 154 154 154 154 154 - Adj. R2 0.174 0.191 0.220 0.223 0.236 - Source: compiled by authors. Note:*p<0.10, **p<0.05, *** p<0.01. From Table no. 9, the robustness test’s regression coefficient results are consistent with previous findings. Ancient nature, modern humanities, and modern nature show significant positive influences, indicating that increasing these three types of cultural perceptions can promote the clustering of homestay businesses, while ancient humanities do not have a Economic Interferences AE Vol. 27 • No. 70 • August 2025 1187 significant effect on their distribution. The results of robustness test of regulation effect are shown in Table no. 10. Table no. 9. Ridge regression analysis robustness test (K=0.5) Coefficient of regression VIF value constant 0.000 (0.000) - X1 0.024 (0.632) 0.287 X2 0.109** (2.347) 0.419 X3 0.127*** (3.666) 0.232 X4 0.130*** (3.449) 0.275 Price 0.092* (1.917) 0.444 Clean 0.055 (1.188) 0.420 Ser&Dec 0.076 (1.613) 0.425 N 154 Adj. R2 0.211 Source: compiled by authors. Note:*p<0.10, **p<0.05, *** p<0.01. From Table no. 10, it is evident that traffic accessibility has a significant positive moderating effect on the clustering of homestays influenced by ancient humanities, ancient nature, and modern nature. It is good transportation links that win a competitive edge. However, the moderating effect on modern humanities is insignificant. This can be attributed to the fact that modern humanities landscapes are primarily located within urban networks, where they closely align with tourist traffic patterns and perceptions of accessibility. Consequently, traffic accessibility exerts a significant moderating effect on the relationship between perceptions of natural landscapes and the distribution of homestays. In contrast, its influence on the distribution associated with perceptions of human-culture landscapes is relatively marginal. Table no. 10. Robustness results of traffic accessibility moderating effect Model 23 Model 24 Model 25 Model 26 Model 27 Model 28 Model 29 Model 30 Model 31 Model 32 Model 33 Model 34 constant 0.000 (0.000) -0.000 (-0.000) -0.026 (-0.368) 0.000 (0.000) 0.000 (0.000) -0.024 (-0.356) 0.000 (0.000) -0.000 (-0.000) -0.025 (-0.346) -0.000 (-0.000) -0.000 (-0.000) -0.035 (-0.525) X1 0.327*** (4.289) 0.323*** (4.380) 0.197** (1.998) X2 0.351*** (4.687) 0.352*** (4.890) 0.242*** (3.218) X3 0.399*** (5.326) 0.392*** (5.417) 0.269** (2.302) X4 0.411*** (5.390) 0.418*** (5.714) 0.298*** (3.631) AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1188 Amfiteatru Economic Model 23 Model 24 Model 25 Model 26 Model 27 Model 28 Model 29 Model 30 Model 31 Model 32 Model 33 Model 34 Traffic 0.283*** (3.382) 0.286*** (3.455) 0.291*** (3.538) 0.312*** (3.937) 0.277*** (3.408) 0.280*** (3.456) 0.300*** (3.738) 0.296*** (3.777) X1* Traffic 0.173* (1.892) X2* Traffic 0.298*** (3.690) X3* Traffic 0.139 (1.336) X4* Traffic 0.216*** (2.939) Price 0.148* (1.969) 0.144* (1.974) 0.163** (2.238) 0.103 (1.370) 0.098 (1.355) 0.157** (2.201) 0.157** (2.152) 0.153** (2.161) 0.165** (2.323) 0.132* (1.814) 0.128* (1.821) 0.168** (2.415) Clean 0.043 (0.446) 0.047 (0.500) 0.063 (0.671) 0.105 (1.083) 0.109 (1.168) 0.121 (1.343) 0.027 (0.290) 0.031 (0.342) 0.043 (0.476) 0.033 (0.349) 0.037 (0.405) 0.044 (0.505) Ser&Dec 0.131 (1.331) -0.018 (-0.167) 0.009 (0.082) 0.131 (1.342) -0.025 (-0.238) 0.006 (0.060) 0.108 (1.126) -0.037 (-0.360) -0.012 (-0.114) 0.083 (0.859) -0.079 (-0.768) -0.027 (-0.264) N 154 154 154 154 154 154 154 154 154 154 154 154 Adj. R2 0.174 0.228 0.241 0.191 0.249 0.308 0.220 0.272 0.276 0.223 0.285 0.320 Source: compiled by authors. Note:*p<0.10, **p<0.05, *** p<0.01. 4. Conclusion This research, through theoretical deduction and empirical analysis, delves into the impact of landscape cultural perception on the spatial distribution of homestay businesses, and arrives at the following conclusions: Firstly, considering landscape cultural perception as an endogenous variable within the homestay distribution model, it manifests a remarkable influence on tourist behavior. The tourist decision - making process can be segmented into three phases. Initially, tourists amass information regarding cultural landscape resources and homestay attributes via media platforms, thereby forming cultural perceptions. Once this information converges with their preferences, it kindles their consumption motivation. Finally, through cognitive and emotional interactions, tourists determine to visit the destination and select a homestay. In this process, transportation accessibility emerges as a pivotal factor. Homestays with superior transportation conditions are more likely to attract tourists, and landscape cultural perception also exerts a significant impact on the spatial distribution of homestays. Secondly, the research categorizes cultural perceptions into four types: ancient humanities, modern humanities, ancient nature, and modern nature. A perception matrix is constructed to probe into the theoretical correlation between landscape resources and cultural perceptions. These four types of cultural perceptions exert varying degrees of influence on the spatial distribution of homestay businesses in the Yangtze River Delta region. The perception of modern humanities has the most pronounced effect on homestay clustering, followed by ancient nature and ancient humanities, while modern nature has the weakest impact. Around modern humanistic landscapes such as the Oriental Pearl Tower, Disneyland, and science museums in Shanghai, homestay businesses are highly concentrated, attracting a large number of tourists, thus demonstrating substantial Economic Interferences AE Vol. 27 • No. 70 • August 2025 1189 commercial value. The moderating effect of transportation accessibility is particularly conspicuous in the relationship between natural landscape perceptions and homestay distribution. Improved transportation conditions enhance tourists' capacity to access natural landscapes, thereby augmenting their perception of these landscapes and further propelling the clustering of homestay businesses. Conversely, transportation accessibility has a relatively weaker moderating effect on the relationship between humanistic landscape perceptions and homestay distribution, as humanistic landscapes are typically situated in urban or densely-populated areas with already convenient transportation. Consequently, further improvements in transportation conditions have a limited impact on the distribution of homestays in these regions. Finally, the research accentuates the indispensable role of digital technology in the cultural tourism industry. Digital platforms, by integrating multifaceted information such as the historical background, cultural features, and tourist reviews of scenic spots, offer tourists comprehensive cultural resource guides. The pre-acquisition of this information not only facilitates tourists in better trip planning but also heightens their awareness and anticipations of the cultural aspects of the destinations, thereby enriching their overall travel experience. Simultaneously, online booking systems streamline the processes of accommodation, ticket, and activity arrangements, enabling tourists to make swifter decisions, reduce uncertainties during the trip, and markedly improve travel efficiency and satisfaction. For homestay businesses, digital tools endow them with highly targeted marketing capabilities. For example, big data analysis enables insights into tourist behavior preferences, facilitating customized promotional strategies (Senyao & Ha, 2022). Additionally, digital platforms furnish homestay owners with efficient management tools, automating processes from real-time booking management to customer relationship maintenance, reducing labor costs, and enhancing operational efficiency. Moreover, the digital feedback mechanism empowers homestay businesses to promptly respond to tourist demands and reviews, continuously optimizing service quality. This data-driven management approach not only bolsters the competitiveness of homestay businesses but also fosters their long-term stable development in regional tourism markets. By integrating digital technology with differentiated strategies for landscape culture, this research provides robust support for the sustainable development of the cultural tourism industry (Pasanchay & Schott, 2021). On one hand, digital tools enable tourists to gain a deeper understanding of the cultural aspects of tourist destinations, promoting broader dissemination of cultural resources; on the other hand, homestay operators, through refined management and targeted marketing, further enhance the interactive effects between scenic spots and homestays, thereby augmenting the overall value of regional tourism. Funding This work was supported by the National Social Science Fund of China under Grant [No. 23BGL319]. AE Digtal Culture and Tourism: Perception of Landscape Culture and Distribution of Homstay 1190 Amfiteatru Economic References Andereck, K.L., Valentine, K.M., Knopf, R.C., and Vogt, C.A., 2005. Residents’ perceptions of community tourism impacts. Annals of tourism research, 32(4), pp.1056-1076. https://doi.org/10.1016/j.annals.2005.03.001. Bridges, J., and Vásquez, C., 2018. If nearly all Airbnb reviews are positive, does that make them meaningless?. Current Issues in Tourism, 21(18), pp.2057-2075. https://doi.org/10.1080/13683500.2016.1267113. Chakravarty, S., 2024. E-Tourism’s Trends and Their Effects on Indian Tourism Industry. In Tanrisever, C., Pamukçu, H., & Sharma, A., Ed.), Future Tourism Trends, pp.97-111. https://doi.org/10.1108/978-1-83753-970-320241006. Goncalves, A.R., Dorsch, L.L.P., and Figueiredo, M., 2022. Digital tourism: An alternative view on cultural intangible heritage and sustainability in Tavira, Portugal. Sustainability, 14(5), art. no. 2912. https://doi.org/10.3390/su14052912. Gupta, V.P., 2024. Leveraging digital transformation in marketing of homestay businesses to promote tourism in the hospitality industry. In Thaichon, P., Dutta, P. K., Chelliah, P.R., Gupta, S. (ed.), Technology and Luxury Hospitality: AI, blockchain and the metaverse, pp.93-115.. https://10.1016/j.heliyon.2024.e29820. Guttentag, D.A., and Smith, S.L.J., 2017. Assessing Airbnb as a disruptive innovation relative to hotels: Substitution and comparative performance expectations. International Journal of Hospitality Management, 2017, 64:1-10. https://doi.org/10.1016/ j.ijhm.2017.02.003. Huan, Y.A.N.G., Yu, Q.I., He, Z.H.A.N.G., Qingyun, Z.H.A.O and Zhonghua, Z.H.A.N.G., 2023. Spatial Differentiation and Influencing Factors of Price Classificaton of Rural Homestays in Shaanxi Province. Economic geography, 43(12), pp.204-211. https://doi.org/10.15957/j.cnki.jjdl.2023.12.020. Inkinen, T., Heikkonen, M., Makkonen, T., and Rautiainen, S., 2024. Multilayered spatial categories in tourism marketing and branding. Journal of Destination Marketing & Management, 31, art. no. 100867. https://doi.org/10.1016/j.jdmm.2024.100867. Ivanov, S., and Webster, C., 2007. Measuring the impact of tourism on economic growth. Tourism Economics, 13(3), pp.379-388. https://doi.org/10.5367/000000007781497773. Jamal, S.A., Othman, N.A., and Muhammad, N.M.N., 2011. The moderating influence of psychographics in homestay tourism in Malaysia. Journal of Travel & Tourism Marketing, 2011, 28(1): 48-61. https://doi.org/10.1080/10548408.2011.535443. Jamaludin, M., Othman, N., and Awang, A.R., 2012. Community based homestay programme: A personal experience. Procedia-Social and Behavioral Sciences, 2012, 42, pp.451-459. https://doi.org/10.1016/j.sbspro.2012.04.210. Jia, M., Kim, H.S., and Tao, S., 2024. B&B customer experience and satisfaction: Evidence from online customer reviews. Service Science, 16(1), pp.42-54. https://doi.org/10.1287/serv.2022.0080. Jones, M.F., Singh, N., and Hsiung, Y., 2015. Determining the critical success factors of the wine tourism region of Napa from a supply perspective. International Journal of Tourism Research, 17(3), pp.261-271. https://doi.org/10.1002/jtr.1984. Economic Interferences AE Vol. 27 • No. 70 • August 2025 1191 KC, B., and Thapa, S., 2024. The power of homestay tourism in fighting social stigmas and inequities. Journal of Sustainable Tourism, pp.1-18. https://doi.org/10.1080/ 09669582.2024.2370974. Li, Z., Huo, M., Huo, T., and Luo, H., 2024. Digital tourism research: A bibliometric visualisation review (2002-2023) and research agenda. Tourism Review, 79(2), pp.273-289. https://doi.org/10.1108/TR-03-2023-0176. Nakayama, C., 2024. Understanding destination marketing processes through film tourism: Local and global networks. Journal of Travel & Tourism Marketing, 41(9), pp.1177-1189. https://doi.org/10.1080/10548408.2024.2404844. Pasanchay, K., and Schott, C., 2021. Community-based tourism homestays’ capacity to advance the sustainable development goals: A holistic sustainable livelihood perspective. Tourism Management Perspectives, 37, art. no. 100784. https://doi.org/10.1016/j.tmp.2020.100784. Qian, Y., Zhu, H., and Wu, J., 2023. Understanding the determinants of where and what kind of home accommodation to build. Ecological Indicators, 154, art. no. 110803. https://doi.org/10.1016/j.ecolind.2023.110803. Qiao, H.H., Wang, C.H., Chen, M.H., Su, C.H.J., Tsai, C.H.K., and Liu, J., 2021. Hedonic price analysis for high-end rural homestay room rates. Journal of Hospitality and Tourism Management, 49, pp.1-11. https://doi.org/10.1016/j.jhtm.2021.08.008. Röslmaier, M., and Ioannides, D., 2023. “Made in Airbnb” sense of localness in Neolocalism: Tourism dynamics on Heimaey, Iceland. Island Studies Journal, 18(2). https://doi.org/10.24043/001c.88998. Senyao, S., and Ha, S., 2022. How social media influences resident participation in rural tourism development: A case study of Tunda in Tibet. Journal of Tourism and Cultural Change, 20(3), pp.386-405. https://doi.org/10.1080/14766825.2020.1849244. Van der Borg, J., Costa, P., and Gotti, G., 1996. Tourism in European heritage cities. Annals of tourism research, 23(2), pp.306-321. https://doi.org/10.1016/01607383(95)00065-8. Wu, Y., and Chen, J., 2023. Spatial distribution heterogeneity and influencing factors of different leisure agriculture types in the city. Agriculture, 13(9), art. no. 1730. https://doi.org/10.3390/agriculture13091730. Xu, X., and Luo, Y., 2023. What makes customers “click”? An analysis of hotel list content using deep learning. International Journal of Hospitality Management, 114, art. no. 103581. https://doi.org/10.1016/j.ijhm.2023.103581. Zeng, Y., Zhang, X., Liu, X., and Li, Z., 2024. Detecting driving factors from spatial spectrums perspective: A multilevel analysis of homestay industry in heterogeneous heritage destinations. Journal of China Tourism Research, pp.1-27. https://doi.org/10.1080/19388160.2024.2404030.