• 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.

• The Journal of Information Systems
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• v.26 no.3
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• pp.187-209
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• 2017

Purpose The sharing economy is the most important keyword that is changing the paradigm of the world economy and management, and it was selected as one of 10 ideas to change the world in 2011 in Time magazine. The purpose of this study is to verify the structural relationship between perceived value and risk, network effect, usefulness, trust, and intention to use of accommodation service. In addition, the effects of utility and trust, and the experience of using accommodation service are controlled by the effect of empirical value and reciprocal value among the perceived value of sharing economic service, The purpose of this study is to propose a plan to activate the accommodation sharing service. Design/methodology/approach This study was designed to investigate the structural relationship between perceived value, perceived risk, network effect, usefulness, trust, and intention to use. Empirical analysis was done using SPSS 21.0 and AMOS 21.0 Findings Based on the results of this study, the following conclusions can be drawn. First, it was concluded that the higher the economic value of the accommodation sharing service is, the more useful and reliable the service is. Second, the higher the experiential value of the accommodation sharing service is, the higher the usefulness and reliability of the service are. Third, it was concluded that the higher the value of the mutual benefit of the accommodation sharing service is, the higher the trust level of the service is, but not the usefulness of the service. Fourth, it was concluded that the higher the perceived risk of accommodation sharing service is, the lower the service trust level is, but the service usability is not affected. Fifth, it was concluded that the larger the network effect of the accommodation sharing service is, the more useful the service is, but it does not affect the reliability of the service. Sixth, it was concluded that the higher the overall reliability of the accommodation sharing service is, the higher the usefulness of the service is. Seventh, it was concluded that the higher the overall usefulness and reliability of the accommodation sharing service are, the higher the intention to use the sharing service is. Finally, in order to test the effect of the experience on the use of trust and usefulness of the accommodation sharing service, multiple group analysis was conducted to examine the relationship between trust and usefulness, It is shown that there is a moderating effect in the path.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.193-200
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• 2021

Recently, posts and opinions on tourist attractions are actively shared on social media. These social big data provide meaningful information to identify objective images of tourist destinations recognized by consumers. Therefore, an in-depth understanding of the tourist image is possible by analyzing these big data on tourism. The study is to analyze destination images in Gangwon-do using big data from social media. It is wanted to understand destination images in Gangwon-do using semantic network analysis and then provided suggestions on how to enhance image to secure differentiated competitiveness as a destination for tourists. According to the frequency analysis results, as tourism in Gangwon-do, Sokcho, Gangneung, and Yangyang were mentioned at a high level in that order, and the purpose of travel was restaurant tour, gourmet food, family trip, vacation, and experience. In particular, it was found that they preferred day trips, weekends, and experiences. Four suggestions were made based on the results. First, it is necessary to develop various types of hotels, accommodation facilities and experience-oriented tour packages. Second, it is necessary to develop a day-to-day travel package that utilizes proximity to the Seoul metropolitan area. Third, it is necessary to promote traditional restaurants and local food. Finally, it is necessary to develop tourist package suitable for healing and family travel. Through this research, the destination image of Gangwon-do was identified and a tourism marketing strategy was presented to improve competitiveness. It also provided a theoretical basis for the use of the big data of tourism consumers in the field of tourism business.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.