This study attempts to scrutinize the fluctuations of the Fijian tourism market and forecast the early warning signals of tourism market vulnerability using the tourism composite indicator (TCI). The data employed on a monthly basis from 2000M01 to 2017M12 and the indicator construction steps were adopted from the ideology of the National Bureau of Economic Research (NBER). A parsimonious macroeconomic and non-economic fundamental determinant are included for the construction of TCI. Subsequently, the procedure then employed the seasonal adjustment using Census X-12, Christiano-Fitzgerald filtering approach, and Bry-Boschan dating algorithm. Empirical evidence highlighted the signalling attributes against Fijian tourism demand with an average lead time of 2.75 months and around 54 percent of directional accuracy rate, which is significant at 5 percent significance level. Thus, the non-parametric technique can forecast the tourism market outlook and the constructed TCI can provide information content from a macroeconomic perspective for policymakers, tourism market players and investors.

The fluctuation of oil price tends to have adverse effect on the tourism industry of a nation. This paper investigates the dynamic changes of the inbound tourism market for Fiji and the driving forces of the Fijian tourism cycle. A set of fundamental determinants of tourism demand including international crude oil price has been utilized to predict the Fijian tourism cycle for the period of 2000-2017. The Markov regime-switching model identifies two distinct phases of the Fijian tourism cycle which are an expansion and a recession period. The filtered and smoothed probabilities signalled the Fijian tourism development significantly with the transition probabilities supported. The adequate dating evaluation can offer essential information for policymakers, tourism industry players and even the community in decision making for Fijian tourism to enhance the nation's development.

The present bibliometric review of research intends to document and synthesize research trends in the domain of sustainable competitiveness over the past decade. Through bibliographical analysis of 1259 Scopus-indexed documents, the literature published from 2010 to 2020 has been identified. Publication output analysis, citation analysis, journal analysis, geographical distribution analysis, and co-occurrence keywords network analysis are utilised in this study to identify the trending research and future direction of this specific field of study using VOSviewer software and Harzing’s Publish and Perish software. Findings revealed that the literature on both sustainability and competitiveness solely is in its growth stage. The most productive countries in this domain are the United States, China, and the United Kingdom. In the retrieved documents, the sustainable competitiveness indeed plays a pivotal part in the evolution of the tourism field and laid a solid foundation for future research. As this paper provides an understanding on the possible mutual reinforcing relationship between two concepts, a stronger linkage on sustainable competitiveness that may catalyse tourism development can offer reference for future research through in-depth analysis.

In line with the 2030 Agenda of Sustainable Development initiated by the United Nations, a climate-resilient development strategy is in a need for the South African tourism. Following the principles of sustainable tourism development, the empirical analysis in this study intends to discover the dynamic relationship between climate change and tourism demand in South Africa. With the adoption of the “Triple Bottom Line” framework, our findings revealed the essential steps for South Africa to address the environmental, social, and economic factors necessary for the development of a sustainable tourism. By adopting the Autoregressive Distributed Lag (ARDL) approach, the present study confirmed that carbon emission leaves a negative impact on the tourism industry in South Africa. Therefore, it is crucial for the tourism practitioners and policy makers to improve the economic efficiency by paying more attention on the carbon dioxide emissions to balance the tourism development and environmental protection for long term sustainable growth for the South African tourism.

This paper examines the impacts of the COVID-19 pandemic and selected commodity variables on Booking.com share price using the Markov-switching approach. Daily data spans from January 2017 through July 2020 are utilized in this study. Empirical evidence showed that COVID-19, international crude oil price, and gold price affected the Booking.com share price significantly. A positive relationship was detected between international crude oil price and gold price towards stock price whereas COVID-19 showed an inverse impact on stock price. The empirical findings evidenced a 1% increase in COVID-19 cases adversely affecting the share price by -0.27%. Our findings also suggested that the potential of another wave of COVID-19 is relatively higher as the bounce back period was identified as 67 days. The filtered and smoothed probabilities signaled the Booking.com share price chronologically, and transition probabilities were identified. Six cycles were outlined, and the effectiveness of the Markov-switching approach in detecting vulnerable financial forecasting was demonstrated. The adequate dating evolution provided satisfactory input for policymakers, investors, and researchers to design and mitigate volatility in commodities and crises.