Background: As of 24th of August 2020, the number of global COVID-19 confirmed cases is nearly 24 million. In the same period, the number of recorded infections in Thailand has remained at approximately 3300. This paper explores the specifics of COVID-19 or SARS-CoV-2 transmissions in Phuket, Thailand's second most visited tourist destination Methods: High-risk contacts recorded by Phuket Provincial Public Health Office were analysed using the Probit model to investigate the risk factors for transmission from confirmed COVID-19 cases to their highrisk contacts. The analysis was further focused on the impact of quarantine measures in state provided facilities on contacts' probability of infection. Findings: 15.6% of 1108 high-risk contacts were found to be infected, and they accounted for 80% of 214 confirmed cases in Phuket till 29th April 2020. Moreover, 10.68% of all high-risk contacts were confirmed to be infected before the quarantine, and 4.55% after the policy was enforced. In addition, a contact who lived within the same household with a confirmed case was 25% more exposed to infection when compared to a contact who did not share a household. Interpretation: Results confirmed that the quarantine policy, which mandated individual isolation in the state provided facilities for all high-risk contacts, diminished contact's chance of infection from the confirmed cases, especially in the epicenter districts. Our findings confirmed that sharing accommodation with an infected case, and exposure to a case with several documented secondary transmission, generally increased the SARS-CoV-2 infection probability. Finally, some confirmed cases do exhibit a higher risk of spreading SARS-CoV-2 to their contacts compared to a typical confirmed case. Further studies of high reproduction groups of infected patients are recommended.
This research sets out to explore the tourists' experiences at local markets in Phuket by analyzing the online reviews from TripAdvisor. This research adopts a novel method by combining the KNIME Analytics Platform with the Naïve Bayes algorithm to undertake the sentiment analysis of 2,934 reviews from seven local markets in Phuket. According to the frequency, salience, and valence, this research has identified five positive and five negative terms to provide a comprehensive understanding of tourists' experiences at these local markets. Moreover, this research also provides a nuanced understanding of their experiences by highlighting the deviation for those positive terms that have negative valence at certain individual market(s) and vice versa. The findings of this research provide significant practical implications for tourism practitioners and local stakeholders.
Purpose A leading characteristic of international tourists at every tourist destination is their role as foreign–income disseminator, and a large number of papers have been dedicated to exploring their behavior. In contrast, this paper aims to shed light on the supply-side of tourism through the study of a hotels’ ability to internationalize their businesses. Design/methodology/approach Based on each hotel’s input data, its efficiency was estimated by a data envelopment analysis approach. Then, the hotel’s intensity of demand from foreign guests was regressed against hotel efficiency along with firm’ control variables. Findings Results from Heckman correction model indicate that ordinary least squares regression would be subject to selection bias, and the results from the correction model strongly indicate a positive linkage between the hotel’s efficiency level and its foreign to total guest ratio, especially in the sub-sample of hotels located in non-tourist destinations. In addition, the results also reveal that the availability of certain services and facilities at hotels are positively related to the number of foreign guests, namely, a spa service and swimming pools. Originality/value Therefore, the main implications from this study are twofold. First, if a hotel’s target market is international travelers, a swimming pool and the availability of a spa service are essential features for hotels in Thailand. Second, policies to improve productivity in hotels should be simultaneously implemented along with tourist-destination-promotion campaigns to optimize the economic impact of international tourist arrivals.
Purpose Based on big data analytical and statistical techniques, this study aims to examine tourists’ shopping experiences at department stores and street markets in Phuket. Design/methodology/approach A Naïve Bayes machine learning algorithm was used to identify the most frequently used terms in TripAdvisor reviews of both department stores and street markets contributed by the same pool of 729 tourists. Findings A total of 18 out of 62 terms used were common in reviews of both shopping settings. However, the study found significant differences in the mean use of the 18 common terms and the likelihood of those terms being used in overall positive reviews. Practical implications The study’s findings indicate differences in tourist shopping experiences at department stores and street markets. Several concrete recommendations are made, including a greater focus on the linkage to the national characteristic of street markets, and particularly the quality of local fruit, to enhance the tourist shopping experience. Originality/value Understanding the differences between shopping malls and street markets from the tourist’s perspective would further enhance the coexistence of shopping malls and street markets in tourism-led growth cities. As such, using reviews of both shopping malls and street markets from an identical pool of tourists, the present study will analyse and compare tourists’ actual shopping experiences, thereby addressing this gap in the research canon via integrated statistical and big data analysis techniques.
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