This study aimed to investigate the perception of, attitude to and practice of linguistic landscape for Korean learning among the international Korean beginners. A questionnaire as a self-assessment instrument was given to a group of 41 international university students with lower Korean language proficiency studying in a Korean university. The descriptive statistics indicated that for perception, although they pay attention to English the most, they could also notice Korean and Romanized Korean on the linguistic landscape (LL). They are positive to the LL as authentic input for Korean learning in terms of attitude. What is more, they could use the LL to enhance their Korean learning particularly in practicing pronunciation and enriching vocabulary, with relatively less practice on grammar checking. This study attested the previous studies concerning the LL as an authentic source for language learning on the one hand, and on the other hand, it showed that even without guidance from the teachers in formal settings, the students could also spontaneously use the LL to learn the Korean language in natural settings.

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This research employs two samples of heritage sites of the Great Wall of China (Ba daling Great Wall and Mu tianyu Great Wall) and their 21000 reviews on TripAdvisor to visualize and induce feature-related comparisons. Word2vec and D3.js are applied for statistical computing and graphing Minimal Spanning Tree (MST) and ThemeRiver. The applications of MST and ThemeRiver are used to delineate outstanding features and clearer feature relationships. In terms of methodology, we applied an innovative research route to combine MST with ThemeRiver to visualize travellers’ online comments. At the same time, the visual results obtained are combined with qualitative analysis to generate valuable, intuitive summaries that can be used for reference in future research. Practically, the results disclose that although both sites are highly enjoyed by tourists, they are significantly different in terms of service, infrastructure and scenery. This article has implications for policymakers and practitioners with regard to making use of online reviews to gather authentic visitor comments on the Great Wall.

Due to the 4th Industrial Revolution, Artificial Intelligence and other internet technologies such as RAISA (Robots, Artificial Intelligence and Service Automation) have been adopted in various industries, including the hospitality and tourism industry. As the industry traditionally offers services through manual labor force, more companies adopt RAISA through a commercial service in the form of chatbots, delivery robots, robot concierge, conveyor restaurants, self-service information/check-in/check-out kiosks, and many others. The current academic resources mainly focus on customer perception or evaluation of robots themselves, and few studies link robot services with customers' overall experience of hospitality services. This study aims to evaluate the current status of RAISA studies in the hotel industry and propose directions for future research. Thirty-seven articles were identified from Google Scholar. These articles were reviewed from four perspectives, namely, journal/year distribution, methodology, research context, theoretical foundation. Findings reveal that most studies on RAISA in the hotel industry focus on RAISA's impacts on the hotel industry, RAISA service quality, customer acceptance of RAISA service, customer satisfaction and factors influencing Customers' Satisfaction on RAISA Service and quantitative approach is the dominant research method. To this end, five research directions cover outstanding themes as follows: 1) strategic assessment of RAISA performance, 2) hotel financial impacts on RAISA employment, 3) customers' satisfaction and RAISA service quality, 4) macro environment impacts on RAISA in hotels, and 5) premium determinants on customers' acceptance on RAISA.