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Wang, W.; Lin, C. Importance of Destination Attributes of Sustainable Urban Waterfronts. Encyclopedia. Available online: https://encyclopedia.pub/entry/56405 (accessed on 28 April 2024).
Wang W, Lin C. Importance of Destination Attributes of Sustainable Urban Waterfronts. Encyclopedia. Available at: https://encyclopedia.pub/entry/56405. Accessed April 28, 2024.
Wang, Wei-Ching, Chung-Hsien Lin. "Importance of Destination Attributes of Sustainable Urban Waterfronts" Encyclopedia, https://encyclopedia.pub/entry/56405 (accessed April 28, 2024).
Wang, W., & Lin, C. (2024, March 19). Importance of Destination Attributes of Sustainable Urban Waterfronts. In Encyclopedia. https://encyclopedia.pub/entry/56405
Wang, Wei-Ching and Chung-Hsien Lin. "Importance of Destination Attributes of Sustainable Urban Waterfronts." Encyclopedia. Web. 19 March, 2024.
Importance of Destination Attributes of Sustainable Urban Waterfronts
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This entry is adapted from the peer-reviewed paper 10.3390/su16062271

The waterfront area refers to the region in a town or city that borders a body of water, emphasizing the relationship between land and water. Destination attributes were grouped into two types: sustainable landscapes (aesthetics, water resource rehabilitation, sustainable lighting, emotional experiences, and low-impact development waterfronts) and sustainable recreational spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experiences). Two destination attributes common to-- both types were identified: nightscapes and waterfronts. 

sustainable urban waterfront destinations urban waterfronts

1. Introduction

Rapid economic development creates many challenges for cities; among these, balancing ecological, economic, and social sustainability is one of the most urgent [1][2][3]. Polluted rivers and damaged natural systems in urban areas have led to the realization that waterfront environments not only create urban ecological diversity [4] but also improve the climate of inland cities that are increasingly affected by climate change [2]. Urban waterfronts are parts of towns and cities that are adjacent to bodies of water, such as rivers or seas [5][6]. They are functional and interactive spaces that link two different systems: land and water [7][8]. Scholars have noted the benefits of successful urban waterfront redevelopment (e.g., forging connections between local residents and visitors and providing spaces for education, healthy living, and wellbeing) [9]. Given the role of sustainable development goals (SDGs) in the development of sustainable cities [10][11], attention is being paid to improving and enhancing the environmental and landscape quality of existing waterfront areas [2][3] and fully using them to provide recreational and tourism functions [12][13] to satisfy the diverse needs of their users. As part of efforts to enhance the quality of urban ecological and social environments, public agencies are looking to make waterfronts attractive tourist destinations in order to stimulate local economic development [13]. Sustainable urban waterfront districts are places where people of all ages and backgrounds can live, work, play, visit, and learn in ways that enhance and celebrate the natural environment and the beauty, diversity, economic vitality, and creativity of a city [8].
Previous studies on urban waterfront destinations have focused on tourism planning [14], tourist experiences [13], and landscaping [7][15]. Few studies have attempted to understand the relationship between tourism and SDGs from the perspective of how sustainable urban waterfronts attract tourists. This question is becoming important given the positive impacts that influencing positive tourist behavior has on economic sustainability (e.g., positive evaluations, word-of-mouth (WOM) intentions, and behavioral intentions) [16][17]. Furthermore, understanding the economic, environmental, and social impacts of tourism requires a detailed examination of a wide range of data [18].
Destination attributes refer to the characteristics and features of a destination that satisfy tourists’ needs [19]. Information about these characteristics and features influence tourists’ behavioral intentions through psychological processes (i.e., perceptions, feelings, and attitudes) [20]. Such information could be obtained through WOM (i.e., face-to-face exchanges of information between tourists’ friends and relatives) [21]. However, with the emergence of the internet, online user-generated content (UGC) and consumer-generated media [21][22], especially comments and ratings, have stimulated information exchange among a much wider range of potential tourists. Well-known travel and search websites such as TripAdvisor, Google Maps, and Lonely Planet provide a large number of online reviews and ratings that could reshape potential tourists’ ideas about a destination, influencing or modifying their destination choices [23]. Scholars have claimed that reviews on these websites represent a more general and widespread form of WOM, namely electronic WOM (eWOM) [24][25], and that the ratings represent customers’ or tourists’ overall satisfaction with the quality, service, or experience of a product or destination [26][27]. For sustainable destination development, eWOM might be more cost-effective [24][28] and influential [29][30] than other sources of information because of the intangible nature of tourism services [31].
Scholars have argued that online reviews could be used as an alternative data source for evaluating sustainable tourism [18]. In the era of big data, tourists leave downloadable qualitative data on the internet through online reviews, photos, and other forms of interaction. However, the influence of online reviews and feedback on destinations offered by public agencies or destination management/marketing organizations (DMOs) has not been explored [32]. Current UGC studies [33][34] have attempted to understand this impact by applying two different approaches (i.e., structured or unstructured), to capture tourist perceptions or awareness [30]. The structured approach relies on quantitative ratings of individual attributes to understand tangible and emotional attributes (e.g., [35]), whereas the unstructured approach relies on open-ended questions that allow respondents to use their own words to freely describe their feelings and perceptions of a destination [36]. The latter approach has been increasingly emphasized by researchers in recent years. However, one of the key challenges is extracting meaningful insights from the large amount of shared text and finding possible patterns and models in this unstructured information [37]. Sparks and Browning [38] have suggested that combining structured quantitative ratings with unstructured textual data could yield a more complete evaluation of users’ online responses and, hence, more useful research results.

2. Sustainable Urban Waterfront Destinations

The waterfront area refers to the region in a town or city that borders a body of water, emphasizing the relationship between land and water [6]. Urban waterfront areas have historically been important for transportation, trade, and recreation. However, many cities worldwide have focused on revitalizing their waterfronts due to population loss and economic decline in urban areas [11]. The renaissance of urban waterfront areas began in North America in the 1960s and then spread to Europe and Australia [4]. The repositioning of abandoned urban waterfront land as a site for commercial, residential, or recreational development aimed to increase the competitiveness of urban economies and promote local tourism development [39].
In recent years, global trends in urban waterfront development have transformed and rebranded districts [4]. In particular, creating themed landscapes has made tourists key targets of urban waterfront areas, which have been transformed into visual spectacles by uplifting theatrical decorations [40] and accelerating commodification [3]. The sustainable revitalization of urban waterfront areas is a crucial aspect of urban development. Many cities are considering the potential benefits and challenges of sustainably redeveloping their waterfronts [6]. The sustainable development of urban waterfronts aims to regenerate underutilized or degraded waterfront areas, involving ecological, cultural, social, economic, and political sustainability [41]. The redeveloped urban waterfronts should better serve local and regional populations and ecosystems [42]. Redeveloped urban waterfronts have diverse identities and multiple meanings, supporting the use of different types of cultural and recreational activities, promoting the wellbeing and quality of life of each individual in a sustainable environment [39][43].
According to Griffin and Hayllar [13] and Jones [44], there are two types of sustainable urban waterfront development from the perspective of sustainable tourism planning. The first of these involves maintaining the original use but incorporating recreation and tourism development; however, the original use remains the most important consideration. The second involves a complete transformation that sets tourism and recreation as the primary goal of development—aiming to create a marketable cultural attraction that promotes public interest through urban festivals and events [45]. Thus, public agencies, DMOs, and local stakeholders act as catalysts for redevelopment by organizing and promoting special urban festivals with which to stimulate local change and attract tourists [3]. Simultaneously, the use of iconic images and symbols is emphasized in order to sell products and experiences, resulting in the “aestheticization of urban space” [46]. Accordingly, urban waterfront areas have become a “themed background” for entertainment, recreation, and tourism [47]. Thus, rehabilitated urban waterfronts not only revitalize and ecologically enhance overlooked urban areas but also reinvent them as destinations favored by the tourism industry [45].

3. S–O–R Theory

The stimulus–organism–response (S–O–R) theory is an extension of the S–R theory, which Mehrabian and Russell [48] applied to environmental psychology to explain and analyze the relationship between the environment and human behavior. This theory suggests that stimuli (S) affect people’s internal affective evaluations (O), which in turn lead to approach or avoidance responses (R). Therefore, the role and relationship of the three components (S, O, and R) are considered in the theory. S are external environmental factors that affect an individual’s internal state and are conceptualized as the effects of stimulating an individual [49]. O are the internal processes and structures that intervene between external stimuli and an individual’s subsequent actions and responses, primarily in terms of emotional and cognitive states [50]. Finally, R, which was originally theorized as a consumer’s “approach or avoidance behaviors” [48], has been conceptualized in empirical studies as a response to an outcome component, conceptualized as a consumer’s final outcome and/or decision, such as the use of behavioral or purchasing intentions as predictors [51][52].
The S–O–R theory has been empirically demonstrated in several studies. Scholars, for example, have used online photos and text in restaurants [51] as S to understand the effect of positive and negative online reviews or to discuss the effect of destination attributes on honeymoon travel satisfaction and experience [52]. This exploratory study applies the core concept of S–O–R theory to discuss the relationship between people and the environment, collect tourists’ destination attributes of urban waterfront areas through online reviews, use the attributes mentioned by tourists in their reviews as environmental stimuli, and use online tourists’ ratings on TripAdvisor and Google Maps as R to understand tourists’ experiences of urban waterfront areas.

4. Destination Attributes

According to Lew [53], destination attributes are an amalgamation of different elements that attract tourists to a destination (e.g., beauty, shopping opportunities, cultural exchanges, infrastructure, safety, and activities). Echtner and Ritchie [19] have proposed a triple bipolar structure for the meaning of destination image, comprising “attributes-holistic”, “functional-psychological”, and “common-unique”. This was proposed in order to highlight how destination attributes are related to the perceptions of a destination and tourists’ experiences [54]. Furthermore, according to Prayag’s [55] definition, destination personality is linked to tourists’ cognition of destination features. This could aid tourists in forming and retaining a collection of positive and distinctive associations with their destinations.
In addition, various attempts have been made to categorize specific destination attributes. Gearing, Swart, and Var [56] categorized destination attributes into the following five main groups:
  • Natural, social, and historical factors;
  • Entertainment and shopping facilities;
  • Infrastructure;
  • Food;
  • Shelter.
In addition, Cooper, Fletcher, Gilbert, and Wanhill [57] categorized destination attributes into a “4A” framework, encompassing attractions, accessibility, amenities, and ancillary services. Buhalis [58] extended this to the “6A” framework by adding available packages and activities. Recently, Moon and Han [59] used 33 items related to local culture, activities and special events, local hospitality, infrastructure, accessibility, destination management, quality of services, and quality of goods purchased. Schlessinger, Cervera-Taulet, and Pérez-Cabaña [60] used nine items to measure destination attributes, including local infrastructure, natural and cultural resources, destination cleanliness, and the hospitality of local people. Regarding sustainable urban waterfronts, scholars suggest that their key attributes include protecting the environment (e.g., natural ecosystems and the recycling of resources) and enhanced present status (e.g., conservation of the genius loci (the historic spirit of a particular space); new public space, views, and perspectives; and a mix of different uses) [7]. Against this background, scholars have called for sustainability studies on urban waterfront development [61][62]. Several researchers have indicated the multidimensional/multi-item nature of destination attributes, which depends on the specific study context [59][60]. In particular, few studies have attempted to measure the attributes of sustainable waterfront destinations in tourist online reviews.
In the past, the development of urban waterfronts focused more on the environmental functions of water infrastructure and less on the social functions of urban space [39]. In addition, through the transformation of urban development, this space provides people with comfortable and healthy recreational and relaxing environmental functions [42]. Past behavioral studies have mentioned that tourists’ final destination decisions are often based on comparing the attributes of alternative destinations; hence, understanding destination attributes is important for DMOs [54]. A destination’s ability to attract tourists often depends on tourists’ perceptions of the potential benefits provided by the destination’s attributes [63]. As tourists engage in related activities at a destination, these attributes become formative elements of their experiences [54]. It has been suggested that destination attributes (accommodation, attraction, beverages, and transportation) significantly influence tourist satisfaction [64]. In addition, some scholars have found that the landscape, nightscape, and walkway of an urban waterfront influence people’s satisfaction with the space [39]. Therefore, the qualities of destination attributes determine tourist satisfaction as well as future revisit intentions, WOM promotion [28][65][66], service experience, and loyalty (e.g., [60]). In competitive markets, destination attributes play a key role in maintaining competitive advantage [16] and have a meaningful impact on forming a destination’s image [54]. In addition, previous research has shown that the associations between specific destination attributes and tourist responses are contextual and should be measured to reflect the specificity of a destination’s characteristics [67]. For example, Hui, Wan, and Ho [68] found that the link between destination attributes and tourists’ overall (dis)satisfaction differed across geographic regions.

5. Text and Data Mining in Online Reviews

The United Nations’ SDGs mention tourism sustainability as an issue of global significance [69]. Accordingly, scholars have argued that balancing the economic, environmental, and social impacts of tourism requires detailed and updated data [21]. Among potential data sources, social media data might be effective for assessing sustainable tourism [18]. Previous studies have emphasized the importance of social media in tourism [70]. With the rapid development of the internet, tourists are actively posting UGC on social media and other online platforms [21][22]. UGC and online reviews enable individuals to provide feedback about destinations while they describe experiences shared by individual users through Web 2.0-connected devices [71]. As UGC about a destination accumulates, its visibility increases [72]. Consumers’ online reviews represent a valuable source of information that influence brand awareness, customer relationships [73], and travel-related decisions [23]. Online review content could be used to understand consumer perceptions, preferences, and attitudes [21]. While scholars have understood the importance of monitoring tourist opinions and satisfaction with destinations [74][75], the representativeness and completeness of UGC data present a challenge for research [76]. Nonetheless, UGC analysis remains a powerful tool [21][75] for planning and marketing travel destinations in the internet era.
According to Jia [21], online ratings and reviews are two common forms of UGC related to tourism and travel. Ratings are graded into five levels and thus comprise direct, quantitative indicators of tourists’ evaluations of each aspect of a supplier’s or destination’s performance and their overall satisfaction. Reviews are short texts that qualitatively state the user’s opinion about a supplier or destination; they are extensions and explanations of ratings and describe the causes of (dis)satisfaction. Prior tourism studies have identified the overall attributes of travel destinations through open-ended questionnaires or interviews [54]; however, the availability of secondary data in the form of online reviews and UGC has made qualitative analytic studies easier to conduct, and these have begun to emerge in abundance [77]. For example, studies have conducted content analyses of textual data from online reviews [78][79]. Content analysis refers to the systematic and objective analysis of text features based on patterns and frequencies in textual data [21]. In tourism research, attributes or features are selected using frequently occurring words or term frequency as an inverse to document frequency [80][81]. In a study of destination attributes, Lu and Stepchenkova [82] analyzed 26 attributes from 373 TripAdvisor reviews in seven categories relating to ecological hotel services. Levy, Duan, and Boo [83] analyzed the content of negative hotel complaints from 10 popular online review sites and found that the most common complaints were related to front desk staff, bathroom, room cleanliness, and guestroom noise issues. In the above cases, the data were manually labeled; however, in recent years, data mining techniques have been used [84].
Data mining is a new technology that has developed rapidly in recent years. This is the process of extracting and recognizing useful information from large-scale databases and obtaining knowledge using statistical, mathematical, artificial intelligence, and machine learning (ML) techniques [85]. It could be used in almost any research field that requires data analysis [86]. In the tourism field, data mining techniques could predict the potential value of each customer, reduce the cost of attracting new customers, and strengthen the relationship between hotels and regular customers [84].
According to Turban et al. [85], data mining is divided into three mutually influential steps:
  • Pre-modeling (research questions, data evaluation, and preparation for data mining);
  • Modeling (selecting analytical techniques, analyzing data, evaluating results, and final model identification);
  • Post-modeling (applying and tracking the outcomes of data mining models).
Moro, Cortez, and Rita [86] suggest that, in the overall modeling process (which includes data comprehension, preparation, modeling, and evaluation), preparing data for modeling is one of the most critical steps, especially the selection of variables that best describe the characteristics of the problem (feature selection).
While data mining focuses on structured numerical data (e.g., highly formatted data in a database), text mining focuses on unstructured and semi-structured text data (i.e., texts in social media feedback that are not defined or organized in any form) [86]. Text mining is a common technique used to analyze online reviews [21]. Typically, research aims to identify hidden patterns in online reviews rather than the text reviews themselves [84]. To achieve this, research questions were modeled through the impact of features (independent variables) on user ratings (dependent variables), demonstrating an example of a supervised learning problem in ML [86]. Given these capabilities, this study adopted an ML-based approach to address its research questions.

References

  1. Giles-Corti, B.; Lowe, M.; Arundel, J. Achieving the SDGs: Evaluating indicators to be used to benchmark and monitor progress towards creating healthy and sustainable cities. Health Policy 2020, 124, 581–590.
  2. Pendlebury, J. The historical urban landscape of the Liverpool waterfront: The three graces in a new perspective. In Waterfronts revisited: European Ports in a Historic and Global Perspective; Porfyriou, H., Sepe, M., Eds.; Routledge: New York, NY, USA, 2017; pp. 108–122.
  3. Xie, P.F.; Gu, K. The changing urban morphology: Waterfront redevelopment and event tourism in New Zealand. Tour. Manag. Perspect. 2015, 15, 105–114.
  4. Gonçalves, A.; Thomas, H. Waterfront tourism and public art in Cardiff Bay and Lisbon’s Park of Nations. J. Policy Res. Tour. Leis. Events 2012, 4, 327–352.
  5. Hussein, R.M.R. Sustainable urban waterfronts using sustainability assessment rating systems. Int. J. Civ. Environ. Struct. Constr. Archit. Eng. 2014, 8, 488–498.
  6. Morrison, A.M.; Maxim, C. World Tourism Cities: A Systematic Approach to Urban Tourism; Routledge: London, UK, 2022.
  7. Giovinazzi, O.; Moretti, M. Port cities and urban waterfront: Transformations and opportunities. TeMaLab J. Mobil. Land Use Environ. 2010, 3, 57–64.
  8. Niemann, B.; Werner, T. Strategies for the sustainable urban waterfront. WIT Trans. Ecol. Environ. 2016, 204, 431–439.
  9. Lagarense, B.E.S.; Walansendow, A. Exploring residents’ perceptions and participation on tourism and waterfront development: The case of Manado waterfront development in Indonesia. Asia Pac. J. Tour. Res. 2015, 20, 223–237.
  10. Blasi, S.; Ganzaroli, A.; De Noni, I. Smartening sustainable development in cities: Strengthening the theoretical linkage between smart cities and SDGs. Sustain. Cities Soc. 2022, 80, 103793.
  11. Dixit, S.K.; Abraham, A. Tourism in Asian Cities; Dixit, S.K., Ed.; Routledge: New York, NY, USA, 2021; pp. 11–30.
  12. Aiesha, R.; Evans, G. VivaCity: Mixed-use urban tourism. In Tourism, Culture & Regeneration; Smith, M., Ed.; CAB International: Cambridge, MA, USA, 2007; pp. 35–48.
  13. Griffin, T.; Hayllar, B. Historic waterfronts as tourism precincts: An experiential perspective. Tour. Hosp. Res. 2007, 7, 3–16.
  14. Doorne, S. Power, participation and perception: An insider’s perspective on the politics of the Wellington waterfront redevelopment. Curr. Issues Tour. 1998, 1, 129–166.
  15. Luka, N. Contested periurban amenity landscapes: Changing waterfront ‘countryside ideals’ in central Canada. Landsc. Res. 2017, 42, 256–276.
  16. Ritchie, J.R.B.; Crouch, G.I. The Competitive Destination: A Sustainable Tourism Perspective; CABI: Wallingford, UK, 2003.
  17. Reyes-Menendez, A.; Correia, M.; Matos, N.; Adap, C. Sustainability Understanding Online Consumer Behavior and eWOM Strategies for Sustainable Business Management in the Tourism Industry. Sustainability 2020, 12, 8972.
  18. Hoffmann, F.J.; Braesemann, F.; Teubner, T. Measuring sustainable tourism with online platform data. EPJ Data Sci. 2022, 11, 41.
  19. Echtner, C.M.; Ritchie, J.R.B. The measurement of destination image: An empirical assessment. J. Travel Res. 1993, 31, 3–13.
  20. Lai, K.; Li, X. Tourism destination image: Conceptual problems and definitional solutions. J. Travel Res. 2016, 55, 1065–1080.
  21. Jia, S. Measuring tourists’ meal experience by mining online user generated content about restaurants. Scand. J. Hosp. Tour. 2019, 19, 371–389.
  22. Wilson, A.; Murphy, H.; Fierro, J.C. Hospitality and travel: The nature and implications of user-generated content. Cornell Hosp. Q. 2012, 53, 220–228.
  23. Tsiakali, K. User-generated-content versus marketing-generated-content: Personality and content influence on traveller’s behavior. J. Hosp. Mark. Manag. 2018, 27, 946–972.
  24. Cantallops, A.S.; Salvi, F. New consumer behavior: A review of research on eWOM and hotels. Int. J. Hosp. Manag. 2014, 36, 41–51.
  25. Goyal, C.; Taneja, U. Electronic word of mouth for the choice of wellness tourism destination image and the moderating role of COVID-19 pandemic. J. Tour. Futures, 2023; ahead-of-print.
  26. Banerjee, S.; Chua, A.Y.K. In search of patterns among travellers’ hotel ratings in TripAdvisor. Tour. Manag. 2016, 53, 125–131.
  27. Mariani, M.; Borghi, M. Environmental discourse in hotel online reviews: A big data analysis. J. Sustain. Tour. 2021, 29, 829–848.
  28. Salah, M.H.A.; Abdou, A.H.; Hassan, T.H.; El-Amin, M.A.-M.M.; Kegour, A.B.A.; Alboray, H.M.M.; Mohamed, A.S.D.; Ali, H.S.A.M.; Mohammed, E.F.A. Power of eWOM and Its Antecedents in Driving Customers’ Intention to Revisit: An Empirical Investigation on Five-Star Eco-Friendly Hotels in Saudi Arabia. Sustainability 2023, 15, 9270.
  29. Mauri, A.G.; Minazzi, R. Web reviews influence on expectations and purchasing intentions of hotel potential customers. Int. J. Hosp. Manag. 2013, 34, 99–107.
  30. Toral, S.L.; Martínez-Torres, M.R.; Gonzalez-Rodriguez, M.R. Identification of the unique attributes of tourist destinations from online reviews. J. Travel Res. 2018, 57, 908–919.
  31. Casalo, L.V.; Flavian, C.; Guinaliu, M.; Ekinci, Y. Do online hotel rating schemes influence booking behaviors? Int. J. Hosp. Manag. 2015, 49, 28–36.
  32. Tucker, C.E. Social networks, personalized advertising, and privacy controls. J. Mark. Res. 2014, 51, 546–562.
  33. Jeong, M.; Jeon, M.M. Customer reviews of hotel experiences through consumer generated media (CGM). J. Hosp. Leis. Mark. 2008, 17, 121–138.
  34. Vermeulen, I.E.; Seegers, D. Tried and tested the impact of online hotel reviews on consumer consideration. Tour. Manag. 2009, 30, 123–127.
  35. Baloglu, S.; Mangaloglu, M. Tourism destination images of Turkey, Egypt, Greece, and Italy as perceived by US-based tour operators and travel agents. Tour. Manag. 2001, 22, 1–9.
  36. Stepchenkova, S.; Li, X.R. Destination image: Do top-of-mind associations say it all? Ann. Tour. Res. 2014, 45, 46–62.
  37. Calheiros, A.C.; Moro, S.; Rita, P. Sentiment classification of consumer generated online reviews using topic modeling. J. Hosp. Mark. Manag. 2017, 26, 675–693.
  38. Sparks, B.A.; Browning, V. The impact of online reviews on hotel booking intentions and perception of trust. Tour. Manag. 2011, 32, 1310–1323.
  39. Wang, Y.; Dewancker, B.J.; Qi, Q. Citizens’ preferences and attitudes towards urban waterfront spaces: A case study of Qiantang riverside development. Environ. Sci. Pollut. Res. 2020, 27, 45787–45801.
  40. Boyer, M.C. The City of Collective Memory; MIT Press: London, UK, 1996.
  41. Fernandes, J.; Chamusca, P.; Pinto, J.; Tenreiro, J.; Figueiredo, P. Urban rehabilitation and tourism: Lessons from Porto (2010–2020). Sustainability 2023, 15, 6581.
  42. Evans, C.; Harris, S.; Taufen, A.; Livesley, J.; Crommelin, L. What does it mean for a transitioning urban waterfront to “work” from a sustainability perspective? J. Urban. Int. Res. Placemaking Urban Sustain. 2022.
  43. Leporelli, E.; Santi, G. From psychology of sustainability to sustainability of urban spaces: Promoting a primary prevention approach for well-being in the healthy city designing. A Waterfr. Case Study Livorno. Sustain. 2019, 11, 760.
  44. Jones, A. On the water’s edge: Developing cultural regeneration paradigms for urban waterfronts. In Tourism, Culture Regeneration; Smith, M., Ed.; CABI: Wallingford, UK, 2007; pp. 143–150.
  45. Craig-Smith, S. The role of tourism in inner-harbor redevelopment: A multinational perspective. In Recreation and Tourism as a Catalyst for Urban Waterfront Redevelopment: An International Survey; Craig-Smith, S., Fagence, M., Eds.; Praeger: Westport, CT, USA, 1995; pp. 16–35.
  46. Kipfer, S.; Keil, R. Planning Inc.? Planning the competitive city in the New Toronto. Antipode 2002, 34, 227–264.
  47. Law, C.M. Introduction. In Tourism in Major Cities; Law, C.M., Ed.; International Thomson Business Press: London, UK, 1996; pp. 1–22.
  48. Mehrabian, A.; Russell, J.A. An Approach to Environmental Psychology; MIT Press: Cambridge, MA, USA, 1974.
  49. Eroglu, S.A.; Machleit, K.A.; Davis, L.M. Atmospheric qualities of online retailing: A conceptual model and implications. J. Bus. Res. 2001, 54, 177–184.
  50. Loureiro, S.; Ribeiro, L. The effect of atmosphere on emotions online shopping intention: Age differentiation. In Conference Book Proceeding of ANZMAC Conference–Marketing in the Age of Consumerism: Jekyll or Hyde? MacCarthy, M., Ed.; ANZMAC: Sydney, Australia, 2011.
  51. Bigne, E.; Chatzipanagiotou, K.; Ruiz, C. Pictorial content, sequence of conflicting online reviews and consumer decision-making: The stimulus-organism-response model revisited. J. Bus. Res. 2020, 115, 403–416.
  52. Chen, G.; So, K.K.F.; Hu, X.; Poomchaisuwan, M. Travel for affection: A stimulus-organism-response model of honeymoon tourism experiences. J. Hosp. Tour. Res. 2022, 46, 1187–1219.
  53. Lew, A.A. A framework for tourist attraction research. Ann. Tour. Res. 1987, 14, 553–575.
  54. Kim, J.H. The antecedents of memorable tourism experiences: The development of a scale to measure the destination attributes associated with memorable experiences. Tour. Manag. 2014, 44, 34–45.
  55. Prayag, G. Exploring the relationship between destination image and brand personality of a tourist destination: An application of projective techniques. J. Travel Tour. Res. 2007, 2, 111–130.
  56. Gearing, C.; Swart, W.; Var, T. Establishing a measure of touristic attractiveness. J. Travel Res. 1974, 12, 1–8.
  57. Cooper, C.; Fletcher, J.; Gilbert, D.; Wanhill, S. Tourism: Principles and Practice; Longman Scientific Technical: Harlow, UK, 1993.
  58. Buhalis, D. Marketing the competitive destination of the future. Tour. Manag. 2000, 21, 97–116.
  59. Moon, H.G.; Han, H. Tourist experience quality and loyalty to an island destination: The moderating impact of destination image. J. Travel Tour. Mark. 2018, 36, 43–59.
  60. Schlesinger, W.; Cervera-Taulet, A.; Pérez-Cabaña, C. Exploring the links between destination attributes, quality of service experience and loyalty in emerging Mediterranean destinations. Tour. Manag. Perspect. 2020, 35, 100699.
  61. Rathnaweera, D.U.; Weerakoon, K.G.P.K. Sustainability of urban recreational waterfront development in Colombo urban area, Sri Lanka. Geogr. Malays. J. Soc. Space 2021, 17, 195–206.
  62. Zaki, S.K.; Hegazy, I.R. Investigating the challenges and opportunities for sustainable waterfront development in Jeddah City. Int. J. Low-Carbon Technol. 2023, 18, 809–819.
  63. Ibrahim, E.E.; Gill, J. A positioning strategy for a tourist destination, based on analysis of customers’ perceptions and satisfactions. Mark. Intell. Plan. 2005, 23, 172–188.
  64. Biswas, C.; Deb, S.K.; Hasan, A.A.T.; Khandakar, M.S.A. Mediating effect of tourists’ emotional involvement on the relationship between destination attributes and tourist satisfaction. J. Hosp. Tour. Insights 2021, 4, 490–510.
  65. Chi, C.G.; Qu, H. Examining the relationship between tourists’ attribute, satisfaction and overall satisfaction. J. Hosp. Mark. Manag. 2009, 18, 4–25.
  66. Ozdemir, B.; Aksu, A.; Ehtiyar, R.; Cizel, B.; Cizel, R.B.; Icigen, E.T. Relationships among tourist profile, satisfaction and destination loyalty examining empirical evidence in Antalya region in Turkey. J. Hosp. Mark. Manag. 2012, 21, 506–540.
  67. Žabkar, V.; Brenčič, M.; Dmitrović, T. Modelling perceived quality, visitor satisfaction and behavioural intentions at the destination level. Tour. Manag. 2010, 31, 537–546.
  68. Hui, T.K.; Wan, D.; Ho, A. Tourists’ satisfaction, recommendation and revisiting Singapore. Tour. Manag. 2007, 28, 965–975.
  69. United Nations World Tourism Organization. Tourism in the 2030 Agenda. 2023. Available online: https://www.unwto.org/tourism-in-2030-agenda (accessed on 24 December 2023).
  70. Sotiriadis, M.D.; Van Zyl, C. Electronic word-of-mouth and online reviews in tourism services: The use of Twitter by tourists. Electron. Commer. Res. 2013, 13, 103–124.
  71. O’Reilly, T.; Battelle, J. Web Squared: Web 2.0 Five Years on; O’Reilly Media, Inc.: Newton, MA, USA, 2009.
  72. Tham, A.; Croy, G.; Mair, J. Social media in destination choice: Distinctive electronic word-of-mouth dimensions. J. Travel Tour. Mark. 2013, 30, 144–155.
  73. Papathanassis, A.; Knolle, F. Exploring the adoption and processing of online holiday reviews: A grounded theory approach. Tour. Manag. 2011, 32, 215–224.
  74. Akehurst, G. User generated content: The use of blogs for tourism organisations and tourism consumers. Serv. Bus. 2009, 3, 51–61.
  75. Royds, D. The big data analysis challenge for landscape architecture. J. Digit. Landsc. Archit. 2018, 3, 191–199.
  76. Lu, W.L.; Stepchenkova, S. User-generated content as a research mode in tourism and hospitality applications: Topics, methods, and software. J. Hosp. Mark. Manag. 2015, 24, 119–154.
  77. Martínez-Torres, M.R.; Rodriguez-Piñero, F.; Toral, S.L. Customer preferences versus managerial decision-making in open innovation communities: The case of Starbucks. Technol. Anal. Strateg. Manag. 2015, 27, 1226–1238.
  78. Cao, Q.; Duan, W.; Gan, Q. Exploring determinants of voting for the ‘Helpfulness’ of online user reviews: A text mining approach. Decis. Support Syst. 2011, 50, 511–521.
  79. Pavlou, P.A.; Dimoka, A. The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Inf. Syst. Res. 2006, 17, 392–414.
  80. Choi, S.; Lehto, X.Y.; Morrison, A.M. Destination image representation on the web: Content analysis of Macau travel related websites. Tour. Manag. 2007, 28, 118–129.
  81. Youn Kim, H.; Yoon, J.H. Examining national tourism brand image: Content analysis of Lonely Planet Korea. Tour. Rev. 2013, 68, 56–71.
  82. Lu, W.L.; Stepchenkova, S. Ecotourism experiences reported online: Classification of satisfaction attributes. Tour. Manag. 2012, 33, 702–712.
  83. Levy, S.E.; Duan, W.; Boo, S. An analysis of one-star online reviews and responses in the Washington, DC, lodging market. Cornell Hosp. Q. 2013, 54, 49–63.
  84. Moro, S.; Rita, P.; Coelho, J. Stripping customers’ feedback on hotels through data mining: The case of Las Vegas Strip. Tour. Manag. Perspect. 2017, 23, 41–52.
  85. Turban, E.; Aronson, J.E.; Liang, T.P.; Sharda, R. Decision Support and Business Intelligence Systems, 8th ed.; Pearson Education: London, UK, 2008.
  86. Moro, S.; Cortez, P.; Rita, P. A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 2014, 62, 22–31.
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Subjects: Hospitality, Leisure, Sport & Tourism
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Wei-Ching Wang
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Chung-Hsien Lin
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Update Date: 20 Mar 2024
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