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This study aims to enhance knowledge on marketing strategies to increase repeat visitors. Furthermore, the authors suggest using appropriate destination information tailored to first-time visitors and repeat visitors as social capital.

This study compares the differences in satisfaction between first-time foreign tourists and those repeat visiting. The authors apply a theoretical framework based on optimal distinctiveness theory (ODT) and consumer knowledge to determine which actions maximize the satisfaction of each group. Then, the relationships among assimilation desire, differentiation desire and satisfaction are quantitatively analyzed.

The results show the difference in the relationship between assimilation and travel satisfaction for first-time visitors and repeat visitors. First-time visitors are satisfied with popular sightseeing spots with higher assimilation level, whereas repeat visitors are satisfied with moderately unpopular sightseeing spots with lower assimilation level. The results clarify that information reflecting only the popularity of destinations is significantly effective for first-time visitors, but unsuitable for repeat visitors. Therefore, it would be possible to propose to repeat visitors a combination of “moderately differentiated” destinations.

The first contribution is that on the basis of Brewer (1991), the quantitative analysis confirmed that the social identity of an individual changes from assimilation to differentiation with the accumulation of experience until the optimal point is found. The second contribution is that we combined several fields such as ODT (Brewer, 1991), familiarity (Alba and Hutchinson, 1987), trial/repeat behavior (Iyengar et al., 2015) and reference groups (Peter & Olson, 2010). The third contribution is that the authors proposed marketing strategies on the basis of the empirical analysis to increase the number of inbound tourists.

Recently, people usually use the internet to obtain travel information, when they plan their travel. They especially want to obtain sightseeing spot information from reviews, but there are huge amounts of reviews of sightseeing spots. Users therefore cannot obtain important information from the reviews easily. As described herein, this paper aims to propose a system that automatically extracts and presents welcome news for sightseeing spots from reviews. This proposed Welcome-news is a “useful information” and “unexpected information” related to travel.

The flow for extracting Welcome-news from reviews is simple: A user inputs a sightseeing spot about which to get information; the system obtains reviews of the sightseeing spot and divides each sentence into reviews; the system extracts sentences including Welcome-news keyword(s), and the sentences become useful information; the system extracts unexpected information from useful information based on clustering, and it becomes Welcome-news; and the system presents all Welcome-news to the user.

This paper reports three findings: extraction of useful information for sightseeing spots based on Welcome-news keywords extracted by our experiment and using support vector machine (SVM); extraction of unexpected information for sightseeing spots by clustering; and automatic presentation of Welcome-news.

Numerous studies have extracted information from reviews based on some keywords. This proposed extraction of Welcome-news for travel not only uses keywords but also clusters based on topics. Furthermore, the proposed keywords include general keywords and unique keywords. The former appears for all kinds of sightseeing spots. The latter appears only for sightseeing spot. The authors extracted general keywords manually, and unique keywords using SVM.

The authors’ examination aims to offer a quantitative perspective on the interrelationships between tourist harassment, the destination image, emotions and destination revisit intent. Furthermore, it explores the moderating role of travelers' experiences and tolerance in the link between tourist harassment, the destination image and revisit intentions.

The authors’ examination seeks to fill this research gap by utilizing a combination of qualitative and quantitative methods to test eight hypotheses using AMOS 23 and PROCESS MARCO.

The findings showed that tourist harassment negatively impacts the destination image and revisit intentions. Moreover, it indicated that tourists' experiences and tolerance moderate the link between harassment, the destination image and revisit intentions for travelers with high levels of experience and tolerance compared to those with low levels.

This article contributes to travel research and service failure recovery research on tourist harassment and its consequences. To this end, it developed and validated a new tourist harassment scale. Moreover, it is the first study that examines the moderating role of visitors' experiences and tolerance on the link between tourist harassment, the destination image and revisit intentions. Finally, this article is the first to empirically offer destination harassment reduction techniques.

Discovering and recommending points of interest are drawing more attention to meet the increasing demand from personalized tours. This paper aims to propose and evaluate two fuzzy-based systems for decision of sightseeing spots considering different conditions.

In the system, the authors considered four input parameters as follows: ambient temperature (AT), air quality (AQ), noise level (NL) and the current number of people (CNP) to decide the sightseeing spots visit or not visit (VNV). The authors call the proposed system: fuzzy-based decision visiting systems (FBDVSs). The authors implemented two systems as follows: FBDVS1 (three input parameters) and FBDVS2 (four input parameters). The authors make a comparison study between FBDVS1 and FBDVS2. The authors evaluate the proposed systems by computer simulations.

From the simulations results, the authors conclude that when CNP is increased, the VNV is increased. However, when AQ and NL are increased, the VNV is decreased. Also, when the AT is around 18°C-26°C, the VNV is the best. Comparing the complexity, the FBDVS2 is more complex than FBDVS1. However, FBDVS2 considers also the AT, which makes the system more reliable.

In the future, the authors would like to make extensive simulations to evaluate the proposed systems and compare the performance of the proposed systems with other systems.

By simulation results, the authors have shown that the proposed system has a good performance and can choose good sightseeing spots.

Focusing on food markets popularity among tourists, the purpose of this paper is to differentiate clusters of tourists based on their behaviour during their visit of these food halls.

Factor-cluster analysis is the segmentation method used to identify food markets groupings related to their motivations, while ANOVA was performed to determine differences between clusters when it comes to perceived authenticity, satisfaction and revisit intention. La Boqueria in Barcelona (Spain) is the paradigmatic food market used to conduct 196 survey respondents based on factor importance.

Two homogenous clusters were identified and labelled as apathetic-market visitors and food-market lovers.

Segmentation proposal provides insights to be taken into account for future Destination Management Organisation’s policies, related to promotional activities and destination branding development. They could create effective tailor-made promotional campaigns and adequate business strategies to further attract more food lovers markets, without removing apathetic-market visitors.

Fill the gap in the existing knowledge on the tourism interest of local markets in destinations and identify through factor-cluster analysis.

The purpose of this paper is to develop and validate scales to measure and benchmark service quality (SQ) in tourism industry.

The second‐order confirmatory factor analysis is employed to validate the instrument. SQ dimensions have been modeled which have significant impact on customer satisfaction (CS) separately from those which do not have a significant impact.

Hospitality, food, logistics, security, and value for money have significant impact on satisfaction, while amenities, core‐tourism experience, hygiene, fairness of price, information centers, culture, distractions, personal information, and pubs do not have a significant impact.

The above pattern may be different in a different destination, and in a different context. However, a major implication of the current findings is that a destination need not have natural cutting edges to be developed as a tourist destination. A destination with good logistics and assurance for security, value for money, impressive hospitality and food, can satisfy a customer.

The scale which has been developed by us will be useful for destination managers to measure the SQ perceptions of tourists and benchmark destinations. The distinction of SQ dimensions with and without the impact on CS could enable a manager to manage these two sets of factors separately.

Unlike previous works, SQ has been modeled in tourism as a second‐order factor, which appears to be a more appropriate approach. The authors have also modeled factors with and without significant impact on satisfaction separately, and the approach does not seem to have precedence in literature. The inclusion of the factor, “Fairness of Price” is also a new contribution to literature.

The purpose of the study is to confirm the influence of each dimension of visitor's sense of place (place identity, place attachment and place dependence) on each dimension of conservation behavioral intention (general behavioral intention and specific behavioral intention) in the context of urban heritage.

This study uses a quantitative methods. Research respondent is individual visitor who meet the research criteria. The strength influence of sense of place dimension (independent variable) on dimension of conservation behavioral intention (dependent variable) was measured by Somers' D correlation. Primary data were analyzed using descriptive statistical techniques.

All correlation coefficient values were below +0.59. It shows that the sense of place dimensions have a weak influence on each dimension of conservation behavioral intention. Weak correlation occurs because of the setting profile (tourism approaches which tends to be material-based) and the type of visitor (casual cultural tourism and sightseeing tourist) which interprets heritage only as an object of relaxation.

Due to the statistical approach used (nonparametric statistical techniques), the results of the study cannot be generalized. Therefore, further studies with different approaches are needed.

This study demonstrates the need for tourism policy in Semarang Old Town (SOT) as a form of value-driven heritage conservation practice to encourage visitors to appreciate the cultural significance of the area.

In the context of urban heritage area, the literature on research topics is still limited. However, the results of this study were different from previous studies. The weak relationship is explained through an approach that considers contextual factors, namely profile settings and visitor profiles.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

The purpose of this paper is to examine the systematic influence of core product features, event operation quality, sport fan identification, and image of host city as a tourism destination on behavioral intentions of international spectator at a Formula One (F1) event held in Malaysia.

A survey form assessing the specified concepts was designed and employed to examine the structural relationships. The research participants were international spectators (n=512) attending the event. Data were randomly split into two halves, one for EFA (n=256) and the other for CFA and SEM (n=256).

Findings revealed that core product feature and sport fan identification factors were significantly (p<0.05) related to both of international spectators’ desire to stay at the event, attend the event in the future, and revisit the country; whereas, event operation quality was only found to be significantly influential of the re-patronage, and destination image was only significantly influential of the desire to stay. The findings highlight the importance and relevance of these concepts in attracting, serving, and retaining international visitors to the event.

Unlike other studies that are centered merely on game and event factors, this study expanded on a bigger ecological spectrum surrounding a F1 event and systematically evaluated the critical concepts and factors affecting international tourist to the event that can be considerable sources of economic growth. The findings provide empirical evidence for event and tourism management and marketing.

This paper aims to examine how consumers make choices when they are faced with a fixed set of available options, consisting of both preferred and less-preferred choices, in the domain of food consumption. Specifically, the paper offers a novel perspective to predict repeated choice decisions in food consumption, which is termed as “pattern-seeking” – a consumption choice pattern that involves a coherent repetitive sequence of sub-groupings or coherently concentrated sub-groupings of options.

Eight experimental studies that contrast the existing theoretical predictions regarding repeated choices (e.g. primacy effect, recency effect, variety vs consistency) against pattern-seeking were conducted using hypothetical and actual food choices.

The results of experimental studies show that an explicit decision pattern (i.e. pattern-seeking) emerges as the most significant predictor of repeated choice in the food consumption domain.

This study offers a novel perspective on how consumers make repeated choices in the domain of food consumption.

The results show that consumers prefer food consumption with a pattern (vs non-pattern). Thus, it would be better to generate marketing activities that allow customers to satisfy their pattern-seeking more easily.

This study advances the literature on repeated food choices by demonstrating that people possess an inherent preference for patterns in food consumption.