case study online hotel booking

Online Hotel Booking Continuous Intention in the Digital Transformation Age: Case of Vietnam

• Conference paper
• First Online: 27 September 2022
• Cite this conference paper

• Bui Thanh Khoa 3 ,
• Nguyen Hoang Nam 3 ,
• Dam Thi Nguyet 3 &
• Bui Thi Bich Phung 3  

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1010))

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With the remarkable development of information technology, especially in tourism, many hotels have increased the need to integrate technology into their overall business. The study aims to determine the relationship between perceived value, belief in third-party booking sites, trust in hotel, and continuous online hotel booking intention. This study carried out qualitative research, including face-to-face interviews; and quantitative research with 543 respondents who had experienced booking on the website. The results pointed out that trust in hotels and belief in third-party booking sites positively influenced the online hotel booking continuous intention. Moreover, the perceived value negatively impacted the relationship between belief in third-party booking sites and online hotel booking continuous intention. Some managerial implications were proposed for accommodation businesses to develop effective business strategies.

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Mahat F, Abdullah D, Bahari KA, Che Azmi N, Mohd Kamal SB, Zainol N (2020) A conceptual model of online hotel booking: the role of online review and online trust towards online booking intention. ESTEEM J Soc Sci Humanit 4:83–92

Google Scholar  

Khoa BT, Ha NM, Ngoc BH (2022) The accommodation services booking intention through the mobile applications of generation Y: an empirical evidence based on TAM2 model. In: Ngoc Thach N, Ha DT, Trung ND, Kreinovich V (eds) Prediction and causality in econometrics and related topics. Springer International Publishing, Cham, pp 559–574. https://doi.org/10.1007/978-3-030-77094-5_43

Ashrianto PD, Yustitia S (2020) The use of social media in searching for information about Papua. Jurnal The Messenger 12. https://doi.org/10.26623/themessenger.v12i2.1939

Dong Y, Ling L (2015) Hotel overbooking and cooperation with third-party websites. Sustainability 7:11696–11712

Article   Google Scholar  

Lien C-H, Wen M-J, Huang L-C, Wu K-L (2015) Online hotel booking: the effects of brand image, price, trust and value on purchase intentions. Asia Pac Manag Rev 20:210–218

Abdullah D, Jayaraman K, Kamal SBM (2016) A conceptual model of interactive hotel website: the role of perceived website interactivity and customer perceived value toward website revisit intention. Procedia Econ Finance 37:170–175

Kimery KM, McCord M (2002) Third-party assurances: mapping the road to trust in e-retailing. JITTA: J Inf Technol Theory Appl 4:63

Fattah RA, Sujono FK (2020) Social presence of Ruangguru in social media during Covid-19 pandemic. J Messenger 12. https://doi.org/10.26623/themessenger.v12i2.2276

Zainal M, Al-Eideh BM (2021) Modeling ethical decision-making behaviors through using information index. J Syst Manag Sci 11:15–28

Kim SY, Kim JU, Park SC (2017) The effects of perceived value, website trust and hotel trust on online hotel booking intention. Sustainability 9:2262

Wang L, Law R, Guillet BD, Hung K, Fong DKC (2015) Impact of hotel website quality on online booking intentions: eTrust as a mediator. Int J Hospit Manag 47:108–115

Rouibah K, Al-Qirim N, Hwang Y, Pouri SG (2021) The determinants of eWoM in social commerce: the role of perceived value, perceived enjoyment, trust, risks, and satisfaction. J Glob Inf Manage (JGIM) 29:75–102

Lee SK, Min SR (2021) Effects of information quality of online travel agencies on trust and continuous usage intention: an application of the SOR model. J Asian Finance, Econ Bus 8:971–982

Propheto A, Kartini D, Sucherly S, Oesman Y (2020) Marketing performance as implication of brand image mediated by trust. Manag Sci Lett 10:741–746

Bijlsma-Frankema K, Woolthuis RK (2005) Trust under pressure: empirical investigations of trust and trust building in uncertain circumstances. Edward Elgar, Cheltenham

Book   Google Scholar  

Kim H-W, Xu Y, Gupta S (2012) Which is more important in Internet shopping, perceived price or trust? Electron Commer Res Appl 11:241–252

Jin Z, Lim C-K (2021) Structural relationships among service quality, systemic characteristics, customer trust, perceived risk, customer satisfaction and intention of continuous use in mobile payment service. J Syst Manag Sci 11:48–64. https://doi.org/10.33168/JSMS.2021.0204

Valdez LE (2021) Socially responsible buyers' online trust on the website and their level of satisfaction. Handbook of research on reinventing economies and organizations following a global health crisis. IGI Global, pp 80–97

Everard A, Galletta DF (2005) How presentation flaws affect perceived site quality, trust, and intention to purchase from an online store. J Manag Inf Syst 22:56–95

Gharib RK, Garcia-Perez A, Dibb S, Iskoujina Z (2019) Trust and reciprocity effect on electronic word-of-mouth in online review communities. J Enterp Inf Manag 33:120–138. https://doi.org/10.1108/jeim-03-2019-0079

Sweeney JC, Soutar GN (2001) Consumer perceived value: the development of a multiple item scale. J Retail 77:203–220. https://doi.org/10.1016/s0022-4359(01)00041-0

Khoa BT, Huynh LT, Nguyen MH (2020) The relationship between perceived value and peer engagement in sharing economy: a case study of ridesharing services. J Syst Manag Sci 10:149–172. https://doi.org/10.33168/JSMS.2020.0210

Chiang C-C, Lee L-Y (2013) An examination of perceived value dimensions of hotel visitors: using exploratory and confirmatory factor analyses. J Int Manag Stud 8:167–174

Khoa BT (2020) The impact of the personal data disclosure’s tradeoff on the trust and attitude loyalty in mobile banking services. J Promot Manag 27:585–608. https://doi.org/10.1080/10496491.2020.1838028

El-Adly MI (2019) Modelling the relationship between hotel perceived value, customer satisfaction, and customer loyalty. J Retail Consum Serv 50:322–332

Chen YS, Chang CH (2012) Enhance green purchase intentions: the roles of green perceived value, green perceived risk, and green trust. Manag Decis 50:502–520

Sharma VM, Klein A (2020) Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying. J Retail Consum Serv 52:101946

Khoa BT, Hung BP, Mohsen H (2022) Qualitative research in social sciences: data collection, data analysis, and report writing. Int J Public Sector Perform Manag 9. https://doi.org/10.1504/ijpspm.2022.10038439

Bilgihan A, Nusair K, Okumus F, Cobanoglu C (2015) Applying flow theory to booking experiences: an integrated model in an online service context. Inform Manag 52:668–678

Sparks BA, Browning V (2011) The impact of online reviews on hotel booking intentions and perception of trust. Tour Manage 32:1310–1323

Kim SH, Bae JH, Jeon HM (2019) Continuous intention on accommodation apps: integrated value-based adoption and expectation–confirmation model analysis. Sustainability 11:1578

Nunnally JC, Bernstein I (1994) The assessment of reliability. Psy Theo 3:248–292

Khoa BT (2020) Electronic loyalty in the relationship between consumer habits, groupon website reputation, and online trust: a case of the groupon transaction. J Theor Appl Inf Technol 98:3947–3960

Ozturk AB, Bilgihan A, Nusair K, Okumus F (2016) What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. Int J Inform Manag 36:1350–1359

Schlosser AE, White TB, Lloyd SM (2018) Converting web site visitors into buyers: how web site investment increases consumer trusting beliefs and online purchase intentions. J Market 70:133–148. https://doi.org/10.1509/jmkg.70.2.133

Paulose D, Shakeel A (2021) Perceived experience, perceived value and customer satisfaction as antecedents to loyalty among hotel guests. J Qual Assur Hosp Tourism 1–35. https://doi.org/10.1080/1528008x.2021.1884930

Mai Chi VT, Paramita W, Ha Minh Quan T (2021) Does customer experience always benefit company? Examining customers' epistemic motivation and interaction with service contexts. Australas Market J. https://doi.org/10.1177/1839334921998867

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Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

Bui Thanh Khoa, Nguyen Hoang Nam, Dam Thi Nguyet & Bui Thi Bich Phung

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Khoa, B.T., Nam, N.H., Nguyet, D.T., Phung, B.T.B. (2022). Online Hotel Booking Continuous Intention in the Digital Transformation Age: Case of Vietnam. In: Yaseen, S.G. (eds) Digital Economy, Business Analytics, and Big Data Analytics Applications. Studies in Computational Intelligence, vol 1010. Springer, Cham. https://doi.org/10.1007/978-3-031-05258-3_31

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DOI : https://doi.org/10.1007/978-3-031-05258-3_31

Published : 27 September 2022

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Online Hotel Booking App Solution Targeting Middle East Region

The USP of the concept was to offer a platform for the app users to identify the best hotels offering competitive rates and confirm the bookings on the fly by paying through credit cards.

The client approached us to design and develop an app in English and Arabic languages targeting tourists traveling to Dubai. Intuz team performed extensive research to acquire competitive hotel room prices for all segments of hotels to provide a seamless booking solution.

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Intuz's team of expert developers and solution architects crafted a comprehensive hotel booking app solution that enabled users to quickly compare rates from different hotels in Dubai, track the nearest hotels and confirm the bookings on the fly by paying online. The client is glad that he has received what Intuz promised him. The target audience widely accepted the app owing to its elegant UI, UX, and unique functionalities.

Streamlined processes for efficient project management

Upon receiving the inquiry, the Intuz team immediately called the client and acknowledged his requirements. The team worked closely with the client to plan development strategies, prepare BRD, and bifurcate essential features. Additionally, the team suggested the client build a robust backend system to meet the business objectives.

Technical Specifications

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Please note you do not have access to teaching notes, the decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance.

International Journal of Contemporary Hospitality Management

ISSN : 0959-6119

Article publication date: 30 December 2020

Issue publication date: 9 August 2021

Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia).

Design/methodology/approach

The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination.

The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia.

Research limitations/implications

This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration.

Originality/value

This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

• Decision tree
• Machine learning
• Online hotel booking
• Big data analysis
• Hotel determinant attributes
• Geographic distance
• The length of stay

Lee, Y. and Kim, D.-Y. (2021), "The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance", International Journal of Contemporary Hospitality Management , Vol. 33 No. 6, pp. 2264-2282. https://doi.org/10.1108/IJCHM-06-2020-0594

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Copyright © 2020, Emerald Publishing Limited

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Home  >  Customers  >  Leading Online Hotel Booking Site  

Leading Online Hotel Booking Site

Leading Online Hotel Booking Site Stops App Denial of Service Issues

savings in infrastructure costs

competitor’s price scraping

bot blocking costs saving

“The thing that all of us enjoy so much is that it just works, where so many times other solutions don’t.”

Solution Architect

One of the world’s leading hotel booking websites assists anyone looking to book hotel accommodation.

Through web scraping, the hotel booking website’s competitors were acutely aware of its price offerings. After exfiltrating the company’s data, the scrapers would then use this data to adjust their pricing to be slightly cheaper.

Early on, the web scrapers used a fairly standard, repetitive pattern that was more easily identifiable by the team. Over an 18-month period, the hotel booking website watched as scraping methods continually increased in their sophistication. Manual IP address-based rate limiting proved to be too basic and was no longer effective. Competitors’ bots were able to easily masquerade as human site visitors and became far more aggressive.

“We started seeing randomized requests from JavaScript-enabled browsers,” the architect observed. “The scrapers were consuming so much of our wholesaler query allocation that we weren’t able to deliver our pricing to legitimate users—a sort of application denial of service.”

“Ordinarily, 1,000 search requests might yield a single booking—a look to book ratio the wholesalers place on us. If competitors’ bots are themselves making those search requests, we quickly exceed that metric. A few wholesalers were lenient and simply called to ask that we investigate, but others were imposing a strict rate limit that we were exceeding.”

“It quickly became clear that we weren’t going to be able to block the scraper bots without devoting a large chunk of in-house resources to that goal. We initially had 2-1/2 people working on the problem fulltime, which conservatively could have cost us $250,000 (USD) in annual staffing costs alone—never mind the other negative impacts it was having on our business. That’s when we learned about Imperva Bot Management (formerly Distil Networks).”

The Results

Deploying Imperva Bot Management with Akamai, HAProxy, and Cisco IPS

The hotel booking website uses Akamai for its content delivery network, coupled with its Dynamic Site Accelerator.

Set up in a typical deployment, requests are made to the hotel booking website’s load balancer, then passed to its Imperva Appliance for inspection. The requests are then looped back into a separate NIP on the load balancer, where balancing evaluation occurs. The requests are then passed back to the origin server application pool. On the return path, the flow is reversed so the Imperva Appliance can carry out HTTP stream injection before returning the responses. All of the above happens in about 3 to 7 milliseconds.

Investment justification

Before implementing Imperva Bot Management, the hotel booking website suffered traffic spikes during very aggressive web scraping periods resulting in application denial of service. With Imperva, site availability remains constant. And the daily scraping activity that once comprised a significant portion of searches sent to wholesalers has been eliminated, so the hotel booking website is able to expand its presence without additional investment.

Using the Imperva Portal, the architect’s analysis showed a saving to the company of 20% on both infrastructure and wholesaler API bandwidth. “The metrics we look at are the proportion of bad bots versus total traffic. But then I also use the CAPTCHA report to reassure us that we’re not mistakenly serving incorrect data. Yesterday we served 283,000 capture forms, and only had 166 attempts supplied to humans.”

Coupled with the hotel booking website’s own logs, the architect likes to use the Imperva Portal’s Threat by Organization Report. After initial deployment, “We saw a lot of activity coming from Google account services. We still get a lot from AWS, so it has been useful to tell Amazon, ‘Hey, someone is misusing your servers to breach our terms of service. Can you stop them?’ And they do.”

Remember the 2-1/2 fulltime staff members who had their fingers in the dyke? They’re now free to perform more strategic duties. In deploying Imperva, the hotel booking website realized an immediate reduction in bot traffic, bandwidth, and overhead costs. As the architect says, “The thing that all of us enjoy so much is that it just works, where so many times other solutions don’t.”

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This GitHub repository hosts a predictive analytics case study aimed at forecasting hotel booking cancellations. It includes EDA, machine learning models (KNN, Decision Trees), and SMOTE for balancing classes. Complete with code, datasets, and a report, it serves as a resource for understanding data science applications in hotel booking management.

HussainM899/Hotel_Booking_Classification_Prediction

Folders and files, repository files navigation, hotel booking prediction - knn | smote | decision tree.

This case study aims to equip you with practical skills in data science, focusing on predicting customer behaviors and booking cancellations in the hotel industry. I've applied EDA, KNN, Decision Tree algorithms, and learn to handle class imbalances using SMOTE.

Notebook Content

In this notebook, the following topics have been covered:

• Data Preprocessing

Exploratory Data Analysis (EDA)

• Predictive Modeling with KNN and Decision Tree

Handling Class Imbalance with SMOTE

• Model Evaluation and Comparison

Dataset Overview

Provided is the dataset of 'INN Hotels,' containing various features related to hotel bookings. The task is to analyze this data to uncover insights and predict booking cancellations.

Preprocessing Steps

• Data Cleaning : Inspect the dataset for anomalies and missing values and handle them appropriately.
• Data Preparation : Apply appropriate preprocessing techniques to prepare the data for analysis, including encoding categorical variables and normalizing/scaling numerical variables.
• Conduct a comprehensive analysis using statistical summaries and visualizations.
• Explore relationships between different features to understand booking trends.

Predictive Modeling

• KNN : Implement the K-nearest neighbors model to predict booking cancellations.
• Decision Tree : Use the Decision Tree algorithm for the same prediction task.
• SMOTE : Apply SMOTE to address class imbalance issues in the dataset.

Model Evaluation

• Evaluate both models based on accuracy, precision, recall, and F1-score.
• Compare the performance before and after applying SMOTE to understand the impact of class balance on the model's performance.
• Implement SMOTE to create synthetic samples of the minority class.
• Retrain models using the balanced dataset to improve model fairness and performance.

Results and Conclusions

The Decision Tree model outperforms the KNN model across all metrics. Applying SMOTE improved the performance of the models, indicating that class imbalance was a significant factor affecting initial model performance.

How to Run the Notebook

To replicate this analysis:

• Ensure that Jupyter Notebook and the following libraries are installed: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
• Clone the repository containing the dataset and the Jupyter notebook.
• Navigate to the project directory and launch Jupyter Notebook.
• Open the Hotel Assignment (Hussain) (1).ipynb file and execute the cells in order.

Dependencies

• Scikit-learn

For questions or feedback, please contact me at [email protected] .

• Jupyter Notebook 100.0%