• Journal of Digital Convergence
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• v.20 no.4
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• pp.269-278
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• 2022

Recently, the travel and lodging industries have been suffering from the spread of COVID-19. But Airbnb recovered its profits with a differentiated strategy. This study identified the characteristics of Airbnb and examined their effects on customer's revisit intention in the Airbnb context. Perceived value, trust about Airbnb, and social norms were considered as the key factors of revisit intention. In addition, the effects of relative attractiveness, brand identification, and enjoyment on perceived value and trust about Airbnb were examined. The proposed research model was tested based on 285 consumers who had Airbnb experience more than twice. The analysis results showed that relative attractiveness, brand identification, and enjoyment had a significant influence on perceived value and trust. Perceived value had a significant influence on revisit intention. However, trust about Airbnb and social norms did not significantly affect revisit intention. Moreover, ths analysis results found no significant moderating effect of share of wallet. Based on the results of this study, Airbnb would establish effective marketing and operation strategies by understanding the formation mechanism of consumer's revisit intention toward Airbnb.

• Knowledge Management Research
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• v.21 no.4
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• pp.89-108
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• 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
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• v.18 no.12
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• pp.231-242
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• 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.

• Knowledge Management Research
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• v.22 no.3
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• pp.55-73
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• 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 Information Systems
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• v.29 no.1
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• pp.1-22
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• 2020

Purpose Along with increasing use of mobile devices, the sharing economy platform services, which generate profits by sharing products owned or labor, have been activated. A representative example of the sharing economy platform service is Airbnb, which connects providers having idle residential space with users. The results revealed that the cases of superhosts and professional companies had a positive and negative effect on user satisfaction, respectively. Based on signaling theory, this study drew the following implications: residential space providers should make an effort to meet the superhost conditions suggested by Airbnb, and offering residential spaces by individual suppliers rather than professional accommodation companies can more heighten user satisfaction. Design/methodology/approach Because sharing behavior is promoted through trust between interested parties, this study aimed to verify the effects of provider characteristics on user satisfaction in a sharing economy platform service. Specifically, it analyzed user satisfaction, according to host attributes (i.e., "superhost or not," gender, and "professional company or not") among the characteristics of Airbnb suppliers. Findings The results revealed that the cases of superhosts and professional companies had a positive and negative effect on user satisfaction, respectively. Based on these analyses results, this study drew the following implications: residential space providers should make an effort to meet the superhost conditions suggested by Airbnb, and offering residential spaces by individual suppliers rather than professional accommodation companies can more heighten user satisfaction.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.73-86
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• 2018

An analysis of Airbnb and Kozaza will show how valuable and rare resource can have an impact on company performances. Airbnb applied the sharing economy platform, and this business model had disrupted the hospitality industry. Thus, this paper will investigate a Korean company called Kozaza which had benchmarked Airbnb. Furthermore, it will apply the theory of VRIO framework, which consists of how valuable, rare, costly to imitate (imitability), and organization (exploited by the organization) to find competitiveness of Kozaza in the hospitality industry. Thus, it will attempt to show that Kozaza's business model of utilizing the unused resource of Hanok (traditional Korean house) and partnerships with Hanokstay, Seoul Metropolitan Government and Soul Tourism Organization and others have enhanced their resource and capability to strengthen their business model. Furthermore, this research will explore how Kozaza can competitively be successful in the future.

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

• Journal of Digital Convergence
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• v.16 no.9
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• pp.375-381
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• 2018

The purpose of this study is to propose and spread the effective direction of collaboration among domestic and external industries. To this end, it chose to study cases in which domestic and foreign companies achieved high value through collaboration. Studies have shown that businesses are also working increasingly to realize shared values as people are increasingly interested in the shared economy. Based on these results, we proposed collaboration with IKEA, a practical furniture brand, and Airbnb, a shared accommodation platform service. Through collaboration, the two companies were able to share their current limitations, and the result was that their social image could be enhanced through the realization of shared economic values. Based on this research in the future, we hope that not only IKEA and Airbnb, but also companies will be able to collaborate to create the value of promoting shared economies.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.1-27
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• 2021

The entire tourism industry is being hit hard by the COVID-19 as a global pandemic. Accommodation sharing services such as Airbnb, which have recently expanded due to the spread of the sharing economy, are particularly affected by the pandemic because transactions are made based on trust and communication between consumer and supplier. As the pandemic situation changes individuals' perceptions and behavior of travel, strategies for the recovery of the tourism industry have been discussed. However, since most studies present macro strategies in terms of traditional lodging providers and the government, there is a significant lack of discussion on differentiated pandemic response strategies considering the peculiarity of the sharing economy centered on peer-to-peer transactions. This study discusses the marketing strategy for individual hosts of Airbnb during COVID-19. We empirically analyze the effect of changes in listing descriptions posted by the Airbnb hosts on listing performance after COVID-19 was outbroken. We extract nine aspects described in the listing descriptions using the Attention-Based Aspect Extraction model, which is a deep learning-based aspect extraction method. We model the effect of aspect changes on listing performance after the COVID-19 by observing the frequency of each aspect appeared in the text. In addition, we compare those effects across the types of Airbnb listing. Through this, this study presents an idea for a pandemic crisis response strategy that individual service providers of accommodation sharing services can take depending on the listing type.

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