Tourism Domain Research Articles

Pre-training language model incorporating domain-specific heterogeneous knowledge into a unified representation

Existing technologies expand BERT from different perspectives, e.g. designing different pre-training tasks, different semantic granularities, and different model architectures. Few models consider expanding BERT from different text formats. In this paper, we propose a heterogeneous knowledge language model (HKLM), a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. To capture the corresponding relations among these multi-format knowledge, our approach uses masked language model objective to learn word knowledge, uses triple classification objective and title matching objective to learn entity knowledge and topic knowledge respectively. To obtain the aforementioned multi-format text, we construct a corpus in the tourism domain and conduct experiments on 5 tourism NLP datasets. The results show that our approach outperforms the pre-training of plain text using only 1/4 of the data. We further pre-train the domain-agnostic HKLM and achieve performance gains on the XNLI dataset.

How dissimilarity attributes at restaurants trigger negative emotions and associated behavioral intentions: the role of attribute performance

PurposeConsumers' perceptions of different aspects pertaining to servicescape and social servicescape at restaurants have received little consideration in the hospitality literature. To fill this gap, the authors develop a model that conceptualizes and empirically examines the impact of dissimilar attributes in restaurants on the development of negative emotions and the influence of negative emotions on consumers' dissatisfaction, which in turn determines consumers' behavioral intentions.Design/methodology/approachThe authors used the moderating impact of restaurant attribute performance to support the link between negative emotions and dissimilar attributes. To achieve the study's goals, the authors conducted two investigations, Study 1 and Study 2, in Bangladesh and China, respectively. For study 1, 600 data were obtained from local Bangladeshi consumers, while for study 2, 396 foreign customers in China were surveyed. The collected data were examined by using Structural Equation Modeling (SEM) approach. The authors utilized IBM Analysis of Moment Structure (AMOS), version 24.0.FindingsBoth studies 1 and 2 found that dissimilar restaurant attributes had significant positive effects on the development of negative emotions, positive effects of negative emotions on consumer dissatisfaction and a positive influence of consumer dissatisfaction on consumers' behavioral intentions. Results of both studies 1 and 2 also showed that restaurant attributes performance positively moderate the relationships between dissimilar attributes and negative emotions.Practical implicationsThe study's empirical results contribute to the body of knowledge in the domains of tourism, consumer psychology and consumer behavior. The study's findings can assist restaurant managers in better understanding how different features related to the servicescape and social servicescape dimensions cause unpleasant emotions and, as a result, influence consumer behavioral intentions.Originality/valueNo preceding research has looked at the link between dissimilar features and negative emotions in the restaurant setting to the authors' knowledge. Also, no previous research has looked at the moderating consequence of restaurant attributes in the association between dissimilar attributes and negative emotions. This research aims to fill those knowledge gap.

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

This study aims to understand the public’s perspectives, sentiments, attitudes, and discourses regarding the adoption, integration, and use of augmented reality and virtual reality in education and in general by analyzing social media data. Due to its nature, Twitter was the selected platform. Over 17 million tweets were retrieved from January 2010 to December 2020 and four datasets were created. Two of them referred to the general use of these technologies and two to their educational use. The data was analyzed using text mining, sentiment analysis (e.g., polarity and emotion detection), and topic modeling methods. TextBlob, Word-Emotion Association Lexicon (EmoLex), Valence Aware Dictionary for Sentiment Reasoning (VADER), and Latent Dirichlet Allocation (LDA) were some of the tools used. Based on the results, the majority of the public were positively disposed toward the general and the educational use of both augmented reality and virtual reality and mostly expressed positive emotions (e.g., anticipation, trust, and joy) when referring to them. In total, 11 topics emerged that were related to education, new technologies, digital and social media use, marketing and advertising, the industrial domain, the health domain, gaming, fitness and exercising, devices, the travel and tourism domain, and software development kits. The educational benefits of augmented reality and virtual reality, their ability to enrich both teaching and learning activities, and their role as effective educational means were evident.

Application of choice models in tourism recommender systems

AbstractChoice models (CM) are proposed in the field of tourism recommender systems (TRS) with the aim of providing algorithms with both a theoretical understanding of tourist's motivations and a certain degree of transparency. The goal of this work is to overcome some of the limitations of current state‐of‐art algorithms used in TRSs by providing: (1) accurate preferences, which are learnt from user choices rather than from ratings, and (2) interpretable coefficients, which are achieved by means of the set of estimated parameters of CM. The study was carried out with a gastronomic data set generated in an ecological experiment in the tourism domain. The performance of CM has been compared with a set of baseline algorithms (rating‐based and ensembles) by using two evaluation metrics: precision and DCG. The CM outperformed the baseline algorithms when the size of the choice set was limited. The findings suggest that CM may provide an optimal trade‐off between theoretical soundness, interpretability and performance in the field of TRS.

Two decades of research on halal hospitality and tourism: a review and research agenda

Purpose This study aims to conduct a synopsis and overview of past research that discussed halal hospitality development and the halal travel market. Design/methodology/approach An extensive systematic review of 108 articles within the hospitality and tourism domain from 2000 to 2021 that were extracted from the Web of Science and Scopus databases was exhaustively assessed. Findings The results are visualised to determine the most thematic domains, contexts and concepts previously discussed. It also computed the contribution of journals, authors and affiliations in fostering the mobility of halal tourism studies. The study attempted to chart new directions and insights for future scholars and academics to expand the scope of halal literature in two ways. It articulated the implications and guidance themes emphasised in previous studies and identified new paths to abreast the contemporary issues of the hospitality and tourism industry, including sustainable consumption trends, community development, technology and smart practises, as well as potential threats and challenges such as islamophobia. Originality/value In view of the fast growth of halal market studies, the direction and contribution of the previous literature produced limited discussions with certain theoretical concepts. This study advises new pathways in which halal tourism development can respond to contemporary issues in hospitality and tourism.

TRS – A Rule Based Personalized Tourism Recommender System

Abstract: In today’s world many people rely on online services. They even plan for their trips by searching places online. However, they usually face problems of being supplied with lots of information. In consequence, they end up wasting a great amount of time in searching and decision making. Getting the expert advice to specify a reliable tourist attraction quickly and consistent to the requirements of each tourist was difficult, and the amount of experienced experts who can advise for tourism issues was insufficient. Providing an effective service in the tourism sector, such as using the technology of computer systems, can be the right solution and very necessary to attract foreign and local tourists to visit the tourist attractions in the country. Travel and Tourism domain is one of the most important economic areas of a nation and recommender systems in this domain would cater to not only the tourists but also to the governments. To save time and improve the tourist experience, the recommender system recommends to a tourist the most attractive places across the country according to their preferences. The application also allows the user to help book hotels. Along with displaying some of the famous attractions in that place.

면세 상품 지불의도 결정요인에 대한 탐구: 롯데면세점 사례를 중심으로

Duty free store business is a well-performing business in the tourism domain. Given the characteristic, it could be valuable to investigate why consumers use duty free store. This research selects willingness to pay as the explained variable. Therefore, the purpose of this research is to investigate the determinants of willingness to pay. This study adopts four attributes (e.g., convenient facility, cleanliness, price fairness, and trust) as the independent variables. Regarding the market share in the duty free business domain, this study chooses Lotte duty free store as the subject. This study uses survey and executed online survey for the data collection. The research participants are domestic customers. 265 observations were used for the data analysis. Regarding the results, willingness to pay of duty free store product is positively affected by convenient facility, cleanliness, and trust. However, this study presents non-significant association between price fairness and willingness to pay.

Stakeholders’ Perceptions of Nature-Based Solutions for Hurricane Risk Reduction Policies in the Mexican Caribbean

Nature-based solutions (NbSs) have long recognized the value of coastal and marine ecosystem management and associated ecosystem services as useful tools for climate change mitigation (e.g., blue carbon) and adaptation (e.g., coastal protection against flooding and storm surges). However, NbSs remain poorly acknowledged and mostly absent from coastal planning for disaster risk reduction policies in the Caribbean, as well as from ex-post disaster reconstruction funds. With the increasing frequency and intensity of hurricanes in the region, NbSs are now more needed than ever. Taking Mexico as a representative case study for the wider Caribbean, we here seek to identify and analyze the barriers and opportunities perceived by relevant stakeholders for mainstreaming coastal-marine NbSs into coastal management and disaster risk reduction policies (e.g., mangroves as green infrastructure) to protect coastal societies and national economies against hurricanes. We conduct semi-structured, in-depth interviews with twenty stakeholders covering academic, governmental, tourism, NGO, coastal planning, and financial domains. Among the twenty-three identified barriers, governance, institutional, financial, and human-capacity aspects are the most dominant perceptions behind the current lack of NbS implementation. Future action for the policy integration of NbSs requires widespread political will and better quantification of both the provision of ecosystem services and their economic benefits under conventional markets.

Exploring Ontology of Tourism: A New-Realist Approach

The aim of this paper is to propose a new-realist ontology of tourism; an ontology that could be used as a foundation for those social sciences which are ready to accept the moderate realist approach. To achieve this goal, an answer to the general question is sought: What is the nature of the tourism reality? This has been broken down into specific issues: What entities make up the domain of tourism? How are they formed and how do they exist in the world? What are their features? And finally: What are the epistemic consequences of the adopted solutions? Our answers are embedded in Searle’s social ontology and his concepts of biological naturalism and social Background. The proposed tourism ontology will also be able to serve as a competitive option to relativist approaches that seem to be gaining the upper hand in the current debate about the state of tourism research.

Should tourists care more about invasive species? International and domestic visitors’ perceptions of invasive plants and their control in New Zealand

Tourism has been implicated in the spread of invasive species, not only through physical means but through invasive species being perpetuated in destinations as part of the tourism landscape. This study reports on a survey of 238 domestic and international tourists visiting the south of New Zealand, with a focus on their knowledge of and attitudes to the management of two invasive plants: wild conifers and Russell lupins. Both plants have profound ecological, economic and environmental impacts but are also increasingly a part of the tourist landscapes in the study region. The survey found significant differences between domestic and international visitors in their levels of ecological knowledge about the invasive plants, with domestic visitors having greater awareness. However, there were also significant differences between international visitors according to origin and ethnicity, with Asian visitors showing lower awareness and also lower willingness to support eradication of the invasives, even after being provided information on the ecological impact of the species. Participants also responded differently to the two species, being less willing to support eradication of the attractive Russell lupin, compared to wild conifers. There are implications for management in terms of the messaging that may be required for different visitor groups around invasive species control. The study also points to the challenge of developing support for the management of charismatic plant species such as Russell lupin that are now firmly located within the tourism domain.

Global tourism, climate change and energy sustainability: assessing carbon reduction mitigating measures from the aviation industry.

As many business activities—especially those associated with the energy-intensive industries—continue to be major sources of greenhouse gas emissions, and hence significantly contributing to global warming, there is a perceived need to identify ways to make business activities eventually carbon neutral. This paper explores the implications of a changing climate for the global tourism business and its intertwining global aviation industry that operates in a self-regulatory environment. Adopting a bibliometric analysis of the literature in the domain of global tourism and climate change (772 articles), the paper reveals the underlying sustainability issues that entail unsustainable energy consumption. The aviation industry as a significant source of carbon emission within the sector is then examined by analyzing the top 20 largest commercial airlines in the world with respect to its ongoing mitigating measures in meeting the Paris Agreement targets. While self-regulatory initiatives are taken to adopt Sustainable Aviation Fuels (SAF) as alternative fuel production and consumption for drastically reducing carbon emission, voluntary alignment and commitment to long-term targets remain inconsistent. A concerted strategic approach to building up complementary sustainable infrastructures among the global network of airports based in various international tourist destination cities to enable a measurable reduction in carbon emission is necessary to achieve a transformational adaptation of a business sector that is of essence to the recovery of the global economy while attempting to tackle climate change in a post-COVID-19 era.

The pandemic of loneliness: designing smart tourism for combating loneliness

Many technologically driven societies currently experience a “pandemic of loneliness“. The purpose of this viewpoint paper is to conceptualize loneliness as a persistent and widespread issue among consumers that has been amplified by the COVID-19 pandemic and increasingly spills over into the travel and tourism domain. This issue creates many challenges for smart tourism design as it implies changed consumer perspectives on smart tourism and needs for smart tourist experiences. This viewpoint paper is based on a review of relevant academic literature and online resources regarding the issue of loneliness, before and during the COVID-19 pandemic, and potential solutions in smart tourism settings. Loneliness has been completely overlooked by the smart tourism research agenda despite its tremendous implications for engagement, value co-creation, and the need for experience enhancement. This viewpoint paper proposes a framework for designing a loneliness-sensitive smart tourism ecosystem (LoSSTE). The purpose of this paper is to start a debate on how to mobilize smart tourism research for combating loneliness in the post-pandemic era and to outline areas for further research.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40558-022-00234-9.

Discovering a tourism destination with social media data: BERT-based sentiment analysis

使用社交媒体数据发现旅游目的地：基于 bert 的情感分析。研究目的这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体（或地点）和用户的感知（或方面）。研究设计/方法/途径我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达（西班牙）的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。研究发现测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。研究局限性我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。实践意义该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。研究原创性本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT（深度学习模型）训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性（消极性）的原因。

Consumer confusion and decision postponement in the online tourism domain: the moderating role of self-efficacy

PurposeInternet emergence has evolved the business of tourism companies, and every tourism company is available online and provides abundant choices, which causes consumers' confusion. The current study aims to investigate the interaction effect of demographic variables on confusion, decision postponement as a negative consequence of confusion and self-efficacy as a moderator in light of the stimulus organism response (SOR) model. Also, an attempt has been made to integrate the smart technology-based solution to deal with consumers' confusion.Design/methodology/approachThree-way ANOVA, Andrew Hayes process, structural equation modelling and random forest algorithm were used to assess the 507 self-filled questionnaire responses for examining consumers' confusion.FindingsThe results reveal that confusion decreases with increased education levels for each gender and age group. Also, for every gender and education level, consumer confusion increases and then decreases. Further, it was observed that self-efficacy effectively moderates the influence of different confusions on decision postponement. An artificial intelligence-enabled predictive model with an accuracy of 71.49% was established, which forecasts the possibility of consumers' decision postponement.Practical implicationsThis study offers the comprehension of how the travel site administrators and stakeholders have used artificial intelligence-enabled solution for consumers' confusion and to reduce decision postponement.Originality/valueTo the best of the authors' awareness, this is one of the first studies to unveil the interaction effect of demographic factors on confusion, assess the role of self-efficacy as a moderator and provide artificial intelligence-enabled solutions for consumers' confusion.

Dhua Kund Waterfall - A Destination of Eco Tourism of South Bihar

Lofty peaks, lush green valleys, chirping birds, mighty rivers and fallings now have been attracting for human beings since ancient time. Waterfall is one of the beautiful landscapes of the Earth. Dhua Kund, plunging 180 m, a destination of eco- tourism is situated 05 km away from NH-02 and 7 km from Sasaram in district of Rohtas, Bihar. The objective of the research paper is to propagate the beauty of Dhua Kund in tourism domain. The research work is based on both primary and secondary data. If the state government facilitate the infrastructure of the study area, a day will come Dhua Kund would be an eco- tourist hub of Bihar.

Affective components of gastronomy tourism: measurement scale development and validation

PurposeOur knowledge of what emotions are elicited explicitly from food consumption and gastronomy experiences in the travel destination and how these emotions establish a relationship with tourists’ behavior is limited. Thus, this study aims to enrich the current knowledge in the gastronomy tourism field from the affective experience perspective and develop a scale to measure tourists’ affective gastronomy experiences (TAGES).Design/methodology/approachBoth qualitative scale development and quantitative scale validation were conducted to ensure the psychometric properties of TAGES.FindingsWith the focus group’s contributions and experts’ validation, 12 gastronomy experience affects were identified in the first stage. In the second stage, a quantitative data collection involving 650 samples helped refine the scale. Finally, a reliable and valid scale with five items measuring TAGES was successfully developed.Originality/valueThis study provides a novel perspective by viewing tourists’ gastronomy experiences through an affective lens. Moreover, this study successfully provides evidence for the psychometric properties of the newly developed TAGES by systematically applying item response theory (IRT) and classical test theory (CTT). This study enriches the gastronomy tourism domain by developing the TAGES and presenting a rigorous and exhaustive investigation of its psychometric properties based on an integration of IRT and CTT. A valid and reliable scale that measures the TAGES fills the gastronomy literature gap and proposes an effective tool for future gastronomy experience studies.

Analyzing Visitors’ Review of Homestays Located in Nature-Based Settings: An NLP Based Approach

Objective: Sentiment analysis techniques such as Natural Language Processing (NLP) provide a powerful tool to analyze textual data. Along with machine learning and other big data methods, these techniques are used in improving customer service quality in different sectors. This paper utilizes sentiment analysis techniques to identify key themes surrounding visitors’ homestay experience in nature-based settings. Methodology/Approach/Scope: Analysis of 2369 TripAdvisor reviews through Structural Topic Modeling (STM) reveals how high rated homestay experiences differ from those rated low on various parameters. Findings/ Implications/Conclusion – The paper contributes to the knowledge on text mining & its application in improving customer service in the hospitality &tourism domain. The research has practical usage for homestay stakeholders and future direction for further research.

Prediction of User Overall Gratification in Indian Tourism Domain on Hotel Classes and Trip Types

The revenue and economy of the country in the past years significantly depend on tourism. The hotel sector's role is even more prominent in tourism. The plans and decisions of tours of users can be recommended with the collaboration of E-commerce and hotel management. The traveling proportion of the population is getting minor over the months due to the worst impact of COVID-19. Thus not just the tourism, the hotel sector is also in vain in terms of revenue. Users' past experiences and opinions help boost their satisfaction levels by providing recommendations and retaining them. The present scenario and stats prove that the selection and decision of hotels have enormous support on user reviews. This research article tries to find and analyze the various aspects that contribute more towards the gratification levels of users in Indian top tourism city hotels listed by the Master and VISA Inc. survey. This survey focuses on the item-item collaborative filtering and regression techniques based on TripAdvisor reviews of recent times. Once the dimensions are known, it helps in improving them and thus even enhances the ratings of Asian continental hotel management. This study proves that the online travel platform helps obtain reviews from users to maintain the travel recommender systems.

A Modified Markov-Based Maximum-Entropy Model for POS Tagging of Odia Text

POS (Parts of Speech) tagging, a vital step in diverse Natural Language Processing (NLP) tasks has not drawn much attention in case of Odia a computationally under-developed language. The proposed hybrid method suggests a robust POS tagger for Odia. Observing the rich morphology of the language and unavailability of sufficient annotated text corpus a combination of machine learning and linguistic rules is adopted in the building of the tagger. The tagger is trained on tagged text corpus from the domain of tourism and is capable of obtaining a perceptible improvement in the result. Also an appreciable performance is observed for news articles texts of varied domains. The performance of proposed algorithm experimenting on Odia language shows its manifestation in dominating over existing methods like rule based, hidden Markov model (HMM), maximum entropy (ME) and conditional random field (CRF).

Tourist Site Classification Using Keyword Matching Approach: Towards the Development of a Personality Based Tourist Recommendation System

The tourism domain consists of emotionally driven experiences between the tourists and their destinations. Psychological aspects of tourists‟ preferences are increasingly becoming an important factor to consider in the development of systems that enhance better recommendations. Studies on analysis of personality traits as well as developed systems on tourism guide/recommendation exist, however, there is less effort towards the classification of tourist sites according to personality traits and its implementation. This study, therefore, attempts to develop a tourist attraction guide through matching keywords from personality types (Big Five personality dimensions) to tourist locations in order to suggest suitable locations. Data on tourist sites were obtained from online sources with a focus on Nigeria tourist sites. The classification and matching of personality type to tourist site was done using trait keywords obtained from reviews, ratings and feedback. The tourist site information was filtered based on the personality type classification in order to deliver information about the tourist site to a user. The system prototype was implemented using HTML, CSS, JavaScript, PHP and MySQL. The resulting system is a web-based application that suggests tourist locations based on the personality preference specified by a user.