• Journal of information and communication convergence engineering
• /
• v.20 no.2
• /
• pp.96-102
• /
• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• Knowledge Management Research
• /
• v.21 no.4
• /
• pp.89-108
• /
• 2020

This study investigates key factors influencing customers' repurchase intention in the context of Airbnb. Positive and negative emotions formed after customer's first-hand experience are identified as vital antecedents in determining consumer's repurchase intention. This study posits authentic experience, amenities, and price fairness as the key characteristics of Airbnb. It clarifies the role of subjective norms and trend-seeking tendency in repurchase decisions. The proposed research model was analyzed for 306 customers with experience in using Airbnb via structural equation model. The analysis results showed that both positive and negative emotions have a significant effect on customer's repurchase intention. The results clarified the role of Airbnb's characteristic components on repurchase decisions. Finally, subjective norms and trend-seeking tendency had no significant impact on customer's repurchase intention. The results of this study are expected to help establish effective strategies for customer experience and marketing to achieve sustainable growth of Airbnb.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.231-242
• /
• 2020

Due to fierce market competition and COVID-19, it becomes increasingly important for sharing economic platform companies to develop a long-term relationship with customers. In this regard, this study explores the mechanism of customer's repurchase decision making in the context of Airbnb. This study posits customer satisfaction and brand image as the key factors in forming customer's repurchase intention toward Airbnb. It also investigates the effects of price fairness, authentic experience, enjoyment, Airbnb trust and host trust on customer's repurchase intention. This study validated the research hypothesis with 154 customers using Airbnb. The analysis results showed that both customer satisfaction and brand image have a significant impact on repurchase intention and explain 62.0% of its variance. Enjoyment, true experience, and Airbnb trust had significant effects on customer satisfaction, while price fairness and host trust had no significant impact on it. The results revealed that price fairness, authentic experience, enjoyment, and Airbnb trust are significantly associated with brand image, while host trust is not significantly related to it. The results of this study are expected to provide academic and practical implications by enhancing the understanding of customer's repurchasing decision in the context of sharing economic platform.

• Proceedings of the Korea Contents Association Conference
• /
• 2019.05a
• /
• pp.211-212
• /
• 2019

The objectives of this research are to test the utility of semiparametric geographically weighted regression (SGWR, a spatial analysis method) in the small-scale urban sample, and to understand the geographic patterns of provision and pricing of sharing economy based accommodations in the tourist city. This paper focused on how network distance to heritage site, to casino, residential unit prices and other five attribute categories determine Airbnb price in Macau SAR, China. Findings show that SGWR models outperformed OLS models. Moreover, comparing with heritage sites, casinos are the stronger factors to drive up Airbnb (including hostels) rooms' provision and their prices; and residential unit prices are not related with the Airbnb price in the attraction clusters in Macau. This research showed a little example for the applications of SGWR in the small city, and for the analysis of online marketplace data as new urban study material. Practically, this study provides some scientific evidence for hosts, guests, urban planners, and policymakers' decision making in Macau.

• Journal of Digital Convergence
• /
• v.20 no.5
• /
• pp.477-488
• /
• 2022

The purpose of this study is to examine how factors that select Airbnb service affect service satisfaction and the moderating effect according to marital status. The subjects of this study are customers who who have used Airbnb services in the metropolitan area. The questionnaire survey was conducted with 150 people, and the results were analyzed and hypothesis testing was performed using Structural Equation Model(SEM). As a result of the study, it has been found that price, online review, and Unique Experience Expectation(UEE) among the factors that selected Airbnb have positive effects on service use satisfaction. In addition, marital status has been found to play a mediating role among price, UEE and customer satisfaction. For single customers, price is an important factor influencing service satisfaction, but for married customers, it is not. In this sense, it is important not only to conduct marketing and promotions considering only gender, but also to provide services according to whether they are single or married.

• Knowledge Management Research
• /
• v.22 no.3
• /
• pp.55-73
• /
• 2021

The sharing economy is considered as a collaborative consumption which enables customers to share unused resources. This study investigated the key factors affecting consumer loyalty in the context of sharing accommodation. Emotions, perceived value and self-image consistency were posited as key antecedents of enhancing customer loyalty. Authentic experience, home amenities, and price fairness were also considered as Airbnb's selection attributes. Airbnb was selected a survey target because it is the largest company in the domain of shared accommodation market. The research model was analyzed for 294 Airbnb customer through structural equation models. Additionally, this paper examine Airbnb customers' experiences by topic modelling method posted on the Naver blog. Based on the understanding of the key factors affecting customer loyalty to sharing accommodation, the analysis results contribute to establish effective marketing and operation strategies by enhancing customer experience.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.3
• /
• pp.201-210
• /
• 2020

The study aims to investigate determinants of performance indicator and perceptions of existing and potential customers in accommodation sharing. This study uses data of Airbnb in Busan and Jeju from January 1 to December 31 in 2018, provided by AirDNA. The total number of listed accommodation sharing were 5,109 accommodations in Busan and 11,502 accommodations in Jeju. More than 90 property types of registered accommodation are subcategorized and re-classified in this study. Study 1 examined current usage and effects of factors on performance indicator in tourism destinations by applying Airbnb data. Study 2 investigated effects of perceived factors on satisfaction, intention to use, loyalty, and tourism competitiveness by applying online survey data. This study applies statistical analyses such as factor and regression analyses, ANOVA, t-test, and MANOVA. Results of Study 1 showed that usage and effects of accommodation sharing differ from regulation that is related to sharing types. Effects also differ based on travel destinations. Results of Study 2 showed how customers perceive accommodation sharing differ from pure meaning of sharing. The results of Study 1 and 2 found significant effects of price and service factors on performance indicator and customer satisfaction. The findings of Study 2 showed significant effects on loyalty and tourism competitiveness.

• Journal of Distribution Science
• /
• v.19 no.7
• /
• pp.113-121
• /
• 2021

Purpose: Technology induces the virtual distribution channel to exist, especially for booking a room online. This situation, indeed, provides an alternative for the customers to book based on their budget through digital platforms. One platform offering competitive prices is virtual hotel operators, such as Airbnb, OYO, RedDoorz, and Airy Rooms. Preferably, after using their platform, the user should be satisfied and loyal. Hence, this investigation aims to prove some associations. The first is between e-satisfaction and e-loyalty. The second is between website quality and e-satisfaction. The final is between website quality and e-loyalty. Research design, data, and methodology: This study is quantitatively designed with the sample of 350 users of the virtual hotel operator applications in Bandar Lampung: Airbnb, OYO, RedDoorz, and Airy, as the samples. Therefore, by denoting this sample size, the structural equation model based on covariance is utilized to examine the three hypotheses proposed. Also, to get the responses, this study uses a survey through a questionnaire. Result: This investigation demonstrates the positive relationship between e-satisfaction and e-loyalty. Additionally, website quality positively associates with e-satisfaction and e-loyalty. Conclusion: The virtual hotel operators must have the superiority on their website-based application to update the information based on the room availability and price, ensure online transaction safety, and facilitate its utilization to maintain long-term satisfaction and loyalty virtually.

• International Journal of Advanced Culture Technology
• /
• v.9 no.2
• /
• pp.72-79
• /
• 2021

This study is to develop a strategy to prepare an improvement strategy according to the environmental change of the hotel. Currently, domestic hotels are implementing marketing through food and beverage as a countermeasure against the sales decrease, and in order to develop effective marketing plan, 5 Force Model environmental analysis and STP analysis are analyzed. 5 Force Model Environmental Analysis showed that domestic hotels are facing various difficulties such as the expansion of accommodation sharing system, the decrease of Chinese tourists due to the THAAD problem, the increase of hotels, the introduction of PMS, the increase of minimum wage, the introduction of 52 hours work week, and the increase in product preference As an STP response strategy to correspond these difficulties, it is necessary to develop products for the main customers of the hotel food and beverage, such as those in the 20s-30s, the workers, smartphones and SNS users. And also hotels should seek ways to lower price of the product to the level desired by the user to compete against substitutes. In conclusion we suggest that hotels are committed to fulfilling their role by meeting guest safety and COVID-19 compliance requirements, but a focus on immediate cleanliness and quarantine against infectious diseases, like Airbnb, will enable greater growth.