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Abstract: Abstract Sailing holiday activities represent a significant portion of the Blue Economy growth in Europe and across the world. Due to the global financial crisis, yacht ownership has declined, but demand for such holiday products remained steady, therefore shifting the yachters profile towards younger and less experienced consumers who prefer to charter boats, rather than own one. Boat chartering offers more flexibility to explore different regions from year to year, but this means that significantly more time must be spent planning the route, since local experience is absent. The tourists’ experience during the initial contemplation and planning phase, taking place weeks or months before an actual trip, and where a broad range of route options needs to be explored, could thus significantly benefit from support given by automated IT tools. Current literature demonstrates a complete lack of research in the development of itinerary recommendation systems in the context of sailing holidays. In this paper, we describe a methodology for the automatic generation of route recommendations, based on the semantic modelling of spatial data, and the determination of realistic sea route options, based on vessel density maps produced from raw AIS data. We demonstrate the implementation and results from this methodology using one of the most popular sailing regions of Greece, namely the Ionian Sea, as a case study. PubDate: 2022-03-15 DOI: 10.1007/s40558-022-00224-x
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Abstract: Abstract Although travelers tend to consider multi-dimensional hotel information when choosing their accommodation, few online travel agency (OTA) websites allow them to express their preferences and expectations for the selection criteria to obtain customized hotel ranking results. The lack of this function makes travelers have to spend extra time and effort in comparing different hotels to make the final decision. To solve this problem, a hotel ranking method considering travelers’ preferences and expectations is proposed based on multi-dimensional hotel information. In the method, considering the travelers’ actual process of hotel reservation through the OTA website, four types of hotel information (i.e., price, rating, location and text comment) are used. To make full use of these information, text mining, prospect theory and multi-attribute decision-making method are integrated into the proposed method. A case study is given to verify the reliability of the proposed method. The proposed method can be embedded into OTA websites to provide decision support for travelers’ hotel reservation, which will reduce the time spent by travelers in hotel search and comparison, thus effectively promote hotel reservation and improve traveler satisfaction. PubDate: 2022-03-07 DOI: 10.1007/s40558-022-00223-y
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Abstract: Abstract Based on the authenticity theory and limited extant research on virtual reality (VR) tourism experience, this study aims to extend authenticity theory by focusing on perceptions of authenticity from postmodernist approach and developing a theoretical framework for tourists participating in VR tourism experience. In-depth interviews were conducted with 28 respondents, and thematic analysis was adopted to analyze the data. Through inductive and deductive data analysis, three main themes are extracted, and six sub-themes are generated, helping to form a framework of authenticity in VR tourism. The findings contribute both to be authenticity theory and VR tourism implication. PubDate: 2022-02-20 DOI: 10.1007/s40558-022-00221-0
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Abstract: Abstract Customer satisfaction is one of the most important measures in the hospitality industry. Therefore, several psychological and cognitive theories have been utilized to provide appropriate explanations of customer perception. Owing to recent rapid developments in artificial intelligence and big data, novel methodologies have presented to examine several psychological theories applied in the hospitality industry. Within this framework, this study combines deep learning techniques with the expectation-confirmation theory to elucidate customer satisfaction in hospitality services. Customer hotel review comments, hotel information, and images were employed to predict customer satisfaction with hotel service. The results show that the proposed fused model achieved an accuracy of 83.54%. In addition, the recall value that predicts dissatisfaction improved from 16.46–33.41%. Based on the findings of this study, both academic and managerial implications for the hospitality industry are presented. PubDate: 2022-02-15 DOI: 10.1007/s40558-022-00222-z
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Abstract: Abstract Previous studies show that search engine query data is a valuable predictor for tourism demand forecasting. The goals of this study are to identify the current positions of hotels in the perception of the customer and to propose a method for practitioners to increase the visibility of consumer's mind perception of hotel brands. The study used volume of travel queries 30 hotel chains in the Turkey constructed from Google Trends and analyzed search query time series data (2014–2018). To visualize the position of brands was conducted social network analysis techniques. The results show that search engine query data regarding hotels reveal the positioning consumer’s mind of hotels. The study offers that Google Trends data is useful. In addition, the study proposes a method for practitioners. Tourism businesses could use search engine data to reveal its place in the consumer’s mind and change the consumer perception over the years. PubDate: 2022-01-11 DOI: 10.1007/s40558-022-00220-1
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Abstract: Abstract To successfully introduce blockchain-enabled booking platforms in the tourism and hospitality industry, providers need to understand their target audiences. We present the results of a survey of 505 US consumers who, in a simulated hotel booking scenario for a leisure trip, picked between traditional Online Travel Agencies (OTA) and a blockchain-enabled booking app with varying degrees of services, discounts, and brand recognition. We find that blockchain-enabled booking apps that meet the following three conditions could attract up to half of the market: (1) offer discounts over OTAs, (2) provide services which go beyond mere booking, and (3) have well-known brand names. In a series of three nested logistic regressions, we investigate the impact of demographic, psychographic, and service-related traveler characteristics. We find that early adopters of blockchain-enabled hotel booking platforms will be young and highly educated. Potential cost savings over OTAs will also attract travelers with lower incomes and from larger households. Other traveler characteristics that facilitate adoption include a high preparedness to take risks, high IT innovativeness, prior familiarity with blockchain technology, and, mediated through IT innovativeness, a high Generalized Sense of Power. Male travelers are more likely than female travelers to be early adopters due to their higher familiarity with blockchain technology. PubDate: 2022-01-03 DOI: 10.1007/s40558-021-00219-0
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Abstract: Abstract Based on the stimulus–organism–response (S–O–R) theory, this study attempts to investigate how the use of 360-degree virtual mountain walking tours can motivate audiences’ intention to take a real walking in the mountains. The survey results from 320 samples after watching a 360-degree virtual video reveal the positive influence of vividness (stimulus) on presence, emotional involvement, flow state, and enjoyment (organism), leading to the intention to take mountain walking tourism (response). This study also examines the interrelationship between four organism variables to explain the underlying mechanism of 360-degree virtual travel experience in stimulating audiences’ visit intention. It shows how technology helps the development of nature-based tourism. It also offers implications for developers to develop influential 360-degree virtual tourism and destination marketers to promote mountain walking tourism. PubDate: 2021-12-02 DOI: 10.1007/s40558-021-00218-1
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In tourism-dependent cities, investigating the spatiotemporal distribution and dynamics of tourist flows is crucial for better urban planning in both steady and perturbed states. In recent years, researchers have started relying more on photo-based, geotagged social data, which offer insights about tourists, popular hotspots, and mobility patterns. However, distinguishing between tourists and locals from this data is problematic since residence information is often not provided. While previous studies rely on heuristic (e.g., period of stay) and probabilistic (Shannon entropy) approaches, this paper proposes a method for classifying tourists and residents based on machine learning (ML) algorithms and considering parameters that could explain the variability between the two (e.g., weather, mobility, and photo content). This approach was applied to Flickr users’ geotagged photos taken in Tokyo’s 23 special wards from July 2008 to December 2019. The results show that stacked ensemble (SE) models are superior to models based on five supervised-learning algorithms, including gradient boosting machine (GBM), generalized linear model (GLM), distributed random forest (DRF), deep learning (DL), and extremely randomized trees (XRT). Temporal entropy (TEN), mobility on workdays, and frequent visits to amusement venues and crowded places influenced how users were classified. While temporal distribution showed similar monthly/hourly patterns, spatial distribution varied. The proposed approach might pave the way for scholars to carry out future tourism research on different topics and subsequently support policymakers in the decision-making process, specifically in urban settings. PubDate: 2021-12-01 DOI: 10.1007/s40558-021-00208-3
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Abstract: Abstract Information and communication technologies (ICTs) have transformed the travel and leisure sector worldwide, yet until now there are no studies presenting international evidence of the different impacts of ICTs (i.e., Internet usage, secure Internet servers, mobile cellular subscriptions, high-technology export, communications as well as computer, and fixed broadband subscriptions) on tourism development (i.e., international traveler arrivals, increased international tourism receipts, and travel and leisure sector returns) by considering countries with different tourism development processes (e.g., high or low tourism development quantile). It is possible that ICTs have diverse or non-linear impacts on countries undergoing varying tourism development processes. Using international data based on a new panel quantile approach, this research thus aims to explore whether ICTs affect tourism development and looks into the possible asymmetric and non-linear relationships among the many variables. Results show that increasing mobile cellular subscriptions, secure Internet servers, and fixed broadband subscriptions have greater positive effects on traveler arrivals. ICTs also asymmetrically and non-linearly influence tourism across different quantiles. Non-global financial sub-periods and developing nations gain benefits from ICTs’ establishment. Lastly, there are geographic differences in the ICTs-tourism nexus. PubDate: 2021-12-01 DOI: 10.1007/s40558-021-00215-4
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Abstract: Abstract Interactions between tourism and social networks are among the most notable phenomena of recent times, generating new approaches, in terms of both analyses of and policies for tourism promotion. Public authorities have been forced to become involved in these new realities, adapting their promotion channels to tourists’ new behaviour patterns and carefully cultivating interactions with them. It is becoming ever more important to create and transmit an image capable of stimulating high levels of engagement. This article analyses the role of one of the most booming social networks, Instagram, applied to the case of Berlin, a leading tourist city. All posts generated over the course of a year on the German capital’s official Instagram account were encoded, and the characteristics of those that generated the most interaction with users in the form of likes and comments were analysed. Our study reveals that posts more directly intended as advertising generate more negative results, while there are differences between the elements capable of generating more likes and more comments, respectively: likes are more general in nature, while comments are more specifically linked to the Berlin brand. These findings suggest important conclusions for the more efficient development of strategies to promote interaction with users. PubDate: 2021-12-01 DOI: 10.1007/s40558-021-00213-6
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Abstract: Abstract In response to the global COVID-19 pandemic, the U.S. hotels have deployed service models that facilitate social distancing by using information technology. Based on emotion theories, this study built a conceptual model that explains consumers’ intentions to use several personal and public technologies for social distancing in hotels (e.g., electronic menus, mobile apps). The model tested relationships between trust in the hotel, trust in system, technology anxiety, anticipated positive and negative emotions, and intentions to use technologies for social distancing. Using data collected from 1000 U.S. hotel guests, it was found that intentions to use technologies for social distancing were predominantly influenced by positive anticipated emotions, which in turn were influenced by trust in the hotel and technology anxiety. Several implications for academia and industry are provided. PubDate: 2021-12-01 DOI: 10.1007/s40558-021-00216-3
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Abstract: Abstract Recommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user’s ratings or choices. But, when a precise RS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user’s observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called SKNN and s-SKNN follow a classical Nearest Neighbour approach, while the other two, Q-BASE and Q-POP PUSH are based on Inverse Reinforcement Learning. SKNN and s-SKNN optimise precision, Q-BASE tries to identify the characteristics of POIs that make them relevant, and Q-POP PUSH, a novel RS here introduced, is similar to Q-BASE but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by SKNN and s-SKNN optimise precision essentially by recommending quite popular POIs. Q-POP PUSH can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of Q-BASE to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of SKNN and Q-POP PUSH are liked more than those produced by Q-BASE. The rationale of that was found in the large percentage of novel recommendations produced by Q-BASE, which are difficult to appreciate. However, Q-BASE excels in recommending items that are both novel and liked by the users. PubDate: 2021-12-01 DOI: 10.1007/s40558-021-00214-5
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Since culture influences expectations, perceptions, and satisfaction, a cross-culture study is necessary to understand the differences between Japan’s biggest tourist populations, Chinese and Western tourists. However, with ever-increasing customer populations, this is hard to accomplish without extensive customer base studies. There is a need for an automated method for identifying these expectations at a large scale. For this, we used a data-driven approach to our analysis. Our study analyzed their satisfaction factors comparing soft attributes, such as service, with hard attributes, such as location and facilities, and studied different price ranges. We collected hotel reviews and extracted keywords to classify the sentiment of sentences with an SVC. We then used dependency parsing and part-of-speech tagging to extract nouns tied to positive adjectives. We found that Chinese tourists consider room quality more than hospitality, whereas Westerners are delighted more by staff behavior. Furthermore, the lack of a Chinese-friendly environment for Chinese customers and cigarette smell for Western ones can be disappointing factors of their stay. As one of the first studies in the tourism field to use the high-standard Japanese hospitality environment for this analysis, our cross-cultural study contributes to both the theoretical understanding of satisfaction and suggests practical applications and strategies for hotel managers. PubDate: 2021-09-01 DOI: 10.1007/s40558-021-00203-8
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Abstract: Abstract In the not-so-distant past, genealogists and family history hobbyists had to lug around heavy boxes, documents, and copious notes and references as they travelled to discover their family roots. Technological advances in mobile devices and applications have created efficiencies and opened paths of exploration that facilitate researching genealogical roots while travelling. This research employs a netnographic approach in studying genealogy blogs, social media, and websites to see how genealogy tourists use mobile devices and apps. Mobile apps used by genealogists are categorized into a taxonomy which shows the plethora of apps and functions that genealogists can rely on before, during and after a trip. The paper then analyzes how smartphone use in general, and mobile app use in particular, affect genealogy tourism. It is found that travelling genealogists use their mobile devices and apps extensively throughout all travel phases to plan and prepare for trips, to conduct and inform their research, and to share their findings. Genealogy tourists also use technology to create and tap into a virtual collective of like-minded others by sharing their knowledge online to help others, acting as a teacher, but at other times may post questions and seek others’ knowledge, as a learner. As such, the study contributes to ongoing efforts to better understand the impact of mobile technologies on travel. PubDate: 2021-09-01 DOI: 10.1007/s40558-021-00206-5
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