Blockchain Propels Tourism Industry—An Attempt to Explore Topics and Information in Smart Tourism Management through Text Mining and Machine Learning
- Identify the topics and their unexplored relations in all publications by text classification modeling using ML.
- Highlight prominent researchers and their networks by bibliometric analysis using Vosviewer.
2. Overview of Blockchain Applications in Tourism
3. Overview of Text Mining with ML and AI
Text Mining Literature Tools
- Extraction of Information
- Discovery of Suitable Text
- Text Analytics
- Fetch word count from title and abstract:
- Remove the punctuation and special characters:
- Word Cloud:
- RQ1: What is the word cloud for abstract, keywords, and predicted keywords of all extracted publications?
- RQ2: What high-frequency keywords are used in all extracted publications collected from Web of Science in their titles and abstracts?
- RQ3: Who are the prominent authors, and how does network analysis relate to them?
- RQ4: What are the co-citation and citation networks among prominent researchers?
- Collection: The information collected by the Web of Science database is used to extract the most recent studies about blockchain in smart tourism. The data included all the inputs that were made from the database. The filtration procedure is utilized to determine the crucial parameter.
- Preparation: After data collection, specific features are needed for this investigation. The primary purpose of filtration is to consider the parameters that can be used for further analysis. In the data analysis process, parameter selection is the crucial step to evaluate the collected data, and a wrong decision may result from the incorrect selection of essential factors. The process of choosing a feature is known as feature selection. It involves selecting the attributes that are useful or impactful for a given problem. By using only pertinent data and eliminating irrelevant data, feature selection is a technique for lowering the input variable to the model. The two main processes in machine learning are feature extraction and feature selection. Although the goal of both feature extraction and feature selection processes is the same, they are very different. The purpose of feature selection is to select the subset of the original features that apply to the given problem.In contrast, feature extraction is focused on creating new ones. It involves selecting the attributes that are useful or impactful for a given problem. Through feature selection, the goal is to reduce the amount of data the model has to fit to minimize its overfitting.
- Selection: Parameters are also “selection criteria” that choose the information enclosed in the benefits point for further research. The parameters considered for this study are abstract, keywords, keywords plus, citations, and authors. These characteristics include sufficient data to emphasize the precise phrases considered in the earlier studies. The collected dataset consists of 72 input variables or parameters, but only abstract, keywords, keywords plus, citations, and authors are considered for this study.
- Visualization: Visualization is placing information into a visual framework, such as a word cloud or two-dimensional graphs, to make communication more straightforward and to quickly understand and conclude an outcome. For this study, word cloud and frequency of the word are considered. A word cloud is the graphical representation that highlights frequently occurring terms more prominently. Similarly, in the frequency of words, words are represented from higher frequency to lower frequency.
5. Experimental Findings
- RQ1: What is the word cloud for abstract, keywords, and predicted keywords of all extracted publications?
- RQ2: What high-frequency keywords are used in all extracted publications collected from Web of Science in their titles and abstracts?
- RQ3: Who are the prominent authors, and how does network analysis relate to them?
- RQ4: What are the co-citation and citation networks among prominent researchers?
7. Practical Implication
- The type of literature review an ML system can perform differs depending on the software used. There are three main types of literature reviews that it can perform: searching, data extraction, and screening.
- To choose the right tool for a particular type of review analysis, choose a set of tools with the necessary features.
- Free and paid tools are also available. However, the choice should be based on the scope of the research.
- Before using a free tool, the researchers must ensure that it has a trial period. A trial period may allow the researchers to try it out and see if it works well. Some free tools might not enable researchers to add more data to their projects.
8. Industry Implications
8.2. Customer Feedback
8.3. Hotel Booking
- Digital Verification: Tourists must use their passports or cloud-based identity verification to handle their personal information for identification . Traditional verification methods are adequate, but they have a single point of failure and raise security and privacy issues. The shortcomings of the conventional method are improved by the blockchain network powered by smart contracts [121,122], which also successfully prevents the exposure of attribute ownership.
- Change Booking: The blockchain method allows the traveler to quickly change their reservation without the involvement of a third party . Hotels are linked together through peer-to-peer networks because of distributed ledger technology. Additionally, there is transparency regarding the cost of booking accommodations, the availability of rooms and services, and customer information privacy.
- Loyalty Rewards Points: In the conventional hotel industry, travelers can only use their reward points to book discounted hotel rooms or receive complimentary hotel amenities [5,124]. As a result, because traditional systems cannot establish a trust bridge between them, it is challenging for the vendor to develop a trustworthy network to take these points as payment. A loyalty rewards network that can be expanded to include external merchants such as malls, movie theaters, and amusement parks may be created using blockchain technology .
- Customer Reviews: Customer feedback is the most crucial component of hospitality and tourism. Hotels and other merchants may modify the customer reviews to promote demand for their lodgings . Customers are therefore duped into engaging in fraudulent activity. Blockchain provides a reliable and transparent system for customer reviews, as discussed in the customer feedback section.
8.4. Car Rental
8.5. Donation Ecosystem
8.6. Immersive Technological Implications in the Tourism Sector
8.7. Digitalization and the Role of SMEs in Tourism
9. Limitations and Future Research
10. Limitations of Blockchain Implementation
- Privacy and Security: Despite the security assurances blockchains make, they are still vulnerable to attack. For instance, if someone wants to access shared data within a particular blockchain, they only need one node to access it. It means that the easiest way to obtain access to this type of blockchain is by hacking its hardware. Unfortunately, this is not the only issue with blockchains. It can also be used to forge transactions.
- Identity theft: One of the main advantages of blockchains is their democratic nature, which allows them to reach a consensus by voting on nodes that have an identity. Criminals can quickly enter a blockchain with multiple devices, and once they have a majority, they can promptly approve transactions. This method ensures that the majority wins. However, there are some issues with consensus algorithm, for example, the exclusion and manipulation of minorities from the network
- Transparency issues: The concept of transparency in the supply chain is a great idea, as it can provide everyone the closure to make ethical decisions. Because if a supply chain is transparent, the data of all its partners and customers will be transferred to the public blockchain. Unfortunately, it is not always a good idea to use public blockchains in a commercial setting. In a retail environment, complete transparency can be very unpleasant, as it allows everyone to see what is happening in the network in real-time. Though they have disadvantages, private blockchains can prevent people from viewing certain transactions. Since they can restrict the number of people participating, they can prevent the public from trusting the product. Like customers, businesses would rather not have their competitors access their data in the supply chain. It would prevent them from stealing their strategies, secrets, and intellectual property.
- Lack of scalability: The scalability of a blockchain grows with it, increasing its vulnerability. Even if this is not convincing, more steps must be taken before implementing blockchain technology into business. One of the most critical factors when implementing blockchain technology is the availability of copies of every transaction on their network. The amount of data that is stored on a blockchain is enormous. It requires a large amount of storage space and the power to process it. Even if all the hardware, software, and digital components are met, regulating the blockchain will be nearly impossible.
- Slow transaction process and energy consumption: The slow transaction speed is a significant issue hindering the adoption of blockchain technology in various applications. Due to its decentralized nature, the nodes must verify every transaction before it can be accepted as a block. In a centralized system, trust is placed in a central body, which can process millions of transactions daily. Despite the various initiatives to improve the speed of transactions on the blockchain, these solutions are still not enough to solve the issue.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Web of Science Categories||Record Count||Percentage|
|Hospitality Leisure Sport Tourism||34||36.17|
|Computer Science Information Systems||15||15.957|
|Computer Science Interdisciplinary Applications||12||12.766|
|Computer Science Theory Methods||12||12.766|
|Green Sustainable Science Technology||8||8.511|
|Engineering Electrical Electronic||7||7.447|
|Computer Science Artificial Intelligence||6||6.383|
|Computer Science Software Engineering||3||3.191|
|Public Environmental Occupational Health||3||3.191|
|Years and Authors||Methodology||Tools||Parameters|
|Fang (2020) ||Proposed blockchain-based Proof of Concept (POC) system for the online review system|
|Kugblenu (2020) ||Developed a private blockchain network for an E-commerce review system using the smart contract||-|
|Alhogail (2020) ||Smart contract-based blockchain network built on the Ethereum platform for e-commerce||-|
|Paul (2021) ||Review analysis of fake review detection on the online review system: traditional and business solution||-||-|
|Mewada (2021) ||She presented thorough research on spotting bogus reviews in e-commerce and hotels.||-|
|Karode (2022) ||She proposed a token-curated registry (TCR) integrated with a blockchain network to detect fake reviews. The smart contract is used to deploy the proposed approach.||-|
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Puri, V.; Mondal, S.; Das, S.; Vrana, V.G. Blockchain Propels Tourism Industry—An Attempt to Explore Topics and Information in Smart Tourism Management through Text Mining and Machine Learning. Informatics 2023, 10, 9. https://doi.org/10.3390/informatics10010009
Puri V, Mondal S, Das S, Vrana VG. Blockchain Propels Tourism Industry—An Attempt to Explore Topics and Information in Smart Tourism Management through Text Mining and Machine Learning. Informatics. 2023; 10(1):9. https://doi.org/10.3390/informatics10010009Chicago/Turabian Style
Puri, Vikram, Subhra Mondal, Subhankar Das, and Vasiliki G. Vrana. 2023. "Blockchain Propels Tourism Industry—An Attempt to Explore Topics and Information in Smart Tourism Management through Text Mining and Machine Learning" Informatics 10, no. 1: 9. https://doi.org/10.3390/informatics10010009