Study on Overseas Employment Training for the Japanese Tourism industry -Focusing on the Case of S University using Big Data and Internships-

2021 ◽  
Vol 88 ◽  
pp. 3-20
Author(s):  
Hye-Youn Kim
Keyword(s):  
Big Data ◽  
Tourism Industry ◽  
Employment Training

scholarly journals Open Innovation and Social Big Data for Sustainability: Evidence from the Tourism Industry

2018 ◽  
Vol 10 (9) ◽  
pp. 3215 ◽  
Author(s):  
Pasquale Del Vecchio ◽  
Gioconda Mele ◽  
Valentina Ndou ◽  
Giustina Secundo
Keyword(s):  
Big Data ◽  
Open Innovation ◽  
Tourism Industry ◽  
Longitudinal Case Study ◽  
Huge Amount ◽  
Knowledge Assets ◽  
Social Big Data ◽  
Tourism Experience ◽  
Innovation Processes

This paper aims to contribute to the debate on Open Innovation in the age of Big Data by shedding new light on the role that social networks can play as enabling platforms for tourists’ involvement and sources for the creation and management of valuable knowledge assets. The huge amount of data generated on social media by tourists related to their travel experiences can be a valid source of open innovation. To achieve this aim, this paper presents evidence of a digital tourism experience, through a longitudinal case study of a destination in Apulia, a Southern European region. The findings of the study demonstrate how social Big Data could open up innovation processes that could be of support in defining sustainable tourism experiences in a destination.

scholarly journals Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 506 ◽  
Author(s):  
Faisal Mehmood ◽  
Shabir Ahmad ◽  
DoHyeun Kim
Keyword(s):  
Big Data ◽  
Real Time ◽  
Recommendation System ◽  
Tourism Industry ◽  
Jeju Island ◽  
Third Party ◽  
User Preference ◽  
Optimal Route ◽  
Route Recommendation ◽  
Travel Route Recommendation

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

scholarly journals Predicting Travel Behaviour of International and Domestic Tourists using Big Data

2019 ◽  
Vol 8 (3) ◽  
pp. 1572-1580
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
San Francisco ◽  
Data Analytics ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Travel Behaviour ◽  
Tourism Industry ◽  
Machine Learning Algorithms ◽  
Data Set

Tourism is one of the most important sectors contributing towards the economic growth of India. Big data analytics in the recent times is being applied in the tourism sector for the activities like tourism demand forecasting, prediction of interests of tourists’, identification of tourist attraction elements and behavioural patterns. The major objective of this study is to demonstrate how big data analytics could be applied in predicting the travel behaviour of International and Domestic tourists. The significance of machine learning algorithms and techniques in processing the big data is also important. Thus, the combination of machine learning and big data is the state-of-art method which has been acclaimed internationally. While big data analytics and its application with respect to the tourism industry has attracted few researchers interest in the present times, there have been not much researches on this area of study particularly with respect to the scenario of India. This study intends to describe how big data analytics could be used in forecasting Indian tourists travel behaviour. To add much value to the research this study intends to categorize on what grounds the tourists chose domestic tourism and on what grounds they chose international tourism. The online datasets on places reviews from cities namely Chicago, Beijing, New York, Dubai, San Francisco, London, New Delhi and Shanghai have been gathered and an associative rule mining based algorithm has been applied on the data set in order to attain the objectives of the study

scholarly journals Internet Marketing in the Brand Technologies of the Tourism Industry

2019 ◽  
Vol 8 (11) ◽  
pp. 2858-2861
Keyword(s):  
Big Data ◽  
Online Advertising ◽  
Internet Marketing ◽  
Tourism Industry ◽  
The Internet ◽  
Online Behavior ◽  
Media Study ◽  
Use Of Internet ◽  
Modern Technologies ◽  
Future Plans

Due to global digitalisation, Internet marketing has long become an integral part of any effective marketing campaign. According to a Zenith Media study, the growth of the global online advertising market in 2019 is only 10%, which is the lowest increase since 2001. Rest and travel is one of the most popular and discussed topics on social networks. We share new impressions, vivid photos, videos, stories, and 90% of them somehow affect the tourism industry. The global digitalization and widespread use of mobile gadgets has changed the very essence of online behavior. We spend most of our free time on the Internet, we are happy to talk about future plans and remember them after their implementation. Thanks to modern technologies and specialized platforms, advertising campaigns on the Internet are launched in a matter of minutes, receiving instant feedback in the form of comments, applications and even sales. Internet marketing has tremendous mechanisms for targeting, analyzing and processing big data. Therefore, the future of the brand, especially in the field of tourism, depends on the use of Internet marketing by enterprises.

scholarly journals Implementasi Bussiness Intelligent dalam e-Tourism Berbasis Big Data

2019 ◽  
Vol 3 (2) ◽  
pp. 163
Author(s):  
Chandra Eko wahyudi Utomo
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
High Growth ◽  
Tourism Industry ◽  
The Internet ◽  
Company Performance ◽  
Work Processes ◽  
Intelligence System ◽  
Huge Data ◽  
Data Explosion

Abstract The use of information technology that is integrated with work processes in an organization has become an absolute necessity. The availability of complete, correct and accurate data and information has become a basic requirement for the survival of an organization. Business Intelligence (BI) is a form of implementation that is able to answer the above needs. BI has been widely used by organizations in managing data and information to support decision making. BI is usually associated with efforts to maximize the performance of an organization. Business Intelligence System is a term that is generally used for the type of application or technology used to assist BI activities, such as collecting data, providing access, and analyzing data and information about company performance. Along with the rapid online-based information systems including e-tourism, creating a huge data explosion on the internet (bigdata). The very high growth of tourism data on the internet can be utilized for the needs of the tourism industry and research needs in the field of tourism. Keywords: intelligent business, e-tourism, big data

scholarly journals Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model

2020 ◽  
Vol 13 (6) ◽  
pp. 73
Author(s):  
Jean-Luc Pradel Mathurin Augustin ◽  
Shu-Yi Liaw
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Customer Service ◽  
Information Search ◽  
Dynamic Pricing ◽  
Recommendation System ◽  
Tourism Industry ◽  
Social Awareness ◽  
Significance Level ◽  
Hierarchy Of Effects

This study intends to extend the hierarchy of effects model into the reality of the tourism industry after incorporation of information and communication technologies. Data analyses were conducted on 260 online questionnaires. The findings indicated consumer behavior follows a three-layer model: Attention-Intention/Desire-Action/Sharing-Social Awareness. Among big data advantages, recommendation system, information search and improved customer service are important to Attention-Intention; information search, dynamic pricing are important to Desire-Action with customer service (lower significance level); only customer service is important to Sharing-Social awareness. This model allows understanding of consumers’ behavior in online tourism as tourists are often sharing their experiences and raise awareness on service quality from e-vendors. Organizations might use big data to guarantee customers’ satisfaction and attract positive feedback particularly from the third layer of behavior.

scholarly journals Forecasting of Visitors Arrived in Taiwan for Tourism Supply Chain Demand using Big Data

2021 ◽  
Vol 9 ◽  
pp. 87-91
Author(s):  
Yi-Hui Liang
Keyword(s):  
Big Data ◽  
Web Search ◽  
Tourism Industry ◽  
Economic Factors ◽  
International Tourist ◽  
Fast Development ◽  
Information And Communication ◽  
Tourist Demand ◽  
Tourism Supply Chain ◽  
Google Search

The fast development of Information and Communication Technology, generate, collect and operate a large amount of data, which is termed big data. The search queries in web search engines can be retrieved by visitors to obtain useful infor-mation for the selected next visiting destinations. Google Trends on Google search engine can evaluate and compare how many times users are searching for specific terms or topics. Otherwise, economic factors, covering income, the rela-tive prices, and relative exchange rate usually influence the international tourist demand. However, there are different conclusions in different settings. Accord-ingly, this work presents the ARIMAX model for modelling and forecasting numbers of international tourists visiting Taiwan from Japan for different pur-poses and provides an analysis of the effects of big data and economic factors. The results can contribute to the decision makers of the tourism industry in Taiwan

Understanding Big Data and Techniques in Cultural Tourism

2022 ◽  
pp. 121-137
Author(s):  
Zafer Türkmendağ
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Cultural Heritage ◽  
Systematic Literature Review ◽  
Cultural Tourism ◽  
Research Area ◽  
Tourism Industry ◽  
Data Types ◽  
Related Research

Big data enriches the experiences of cultural tourism visitors as well as being used in the management, presentation, and protection of cultural heritage. Technological innovations and the production of more data every day have increased the importance of data and information in competition in the tourism industry. For this, since it is seen that it is important to examine issues such as big data and analytics in cultural tourism, this book chapter presents the studies in the related research area in detail. As a result of the systematic literature review, data types that can be the basis for the formation of big data in cultural tourism and technologies that can support are specified. In addition, researches on cultural heritage and cultural tourism were examined, and theoretical and practical suggestions were presented.

A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sayeh Bagherzadeh ◽  
Sajjad Shokouhyar ◽  
Hamed Jahani ◽  
Marianna Sigala
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Tourism Industry ◽  
Bag Of Words ◽  
Machine Learning Algorithm ◽  
End User ◽  
Content Type ◽  
Customer Reviews

Purpose Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons. Design/methodology/approach Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons. Findings The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment. Practical implications The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences. Originality/value This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

scholarly journals Tourism Experience and Construction of Personalized Smart Tourism Program Under Tourist Psychology

2021 ◽  
Vol 12 ◽  
Author(s):  
Feiya Lan ◽  
Qijun Huang ◽  
Lijin Zeng ◽  
Xiuming Guan ◽  
Dan Xing ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Field Investigation ◽  
Big Data Analysis ◽  
Tourism Industry ◽  
Literature Analysis ◽  
Tourism Experience ◽  
Urban Security ◽  
Travel Services ◽  
Smart Tourism

The present work aims to boost tourism development in China, grasp the psychology of tourists at any time, and provide personalized tourist services. The research object is the tourism industry in Macau. In particular, tourists' experiences are comprehensively analyzed in terms of dining, living, traveling, sightseeing, shopping, and entertaining as per their psychological changes using approaches including big data analysis, literature analysis, and field investigation. In this case, a model of tourism experience formation path is summarized, and a smart travel solution is proposed based on psychological experience. In the end, specific and feasible suggestions are put forward for the Macau tourism industry. Results demonstrate that the psychology-based smart travel solution exerts a significant impact on tourists' tourism experience. Specifically, the weight of secular tourism experience is 0.523, the weight of aesthetic tourism experience is 0.356, and the weight of stimulating tourism experience is 0.121. Tourists prefer travel destinations with excellent urban security and scenic authenticity. They give the two indexes comprehensive scores of 75.14 points and 73.12 points, respectively. The proposed smart travel solution can grasp the psychology of tourists and enhance their tourism experiences. It has strong practical and guiding significances, which can promote constructing smart travel services in Macau and enhancing tourism experiences.

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