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Building on a TripAdvisor data for five Mediterranean destinations, namely, Greece, Croatia, Italy, France and Spain, this study analyses the constituents of restaurants' online reputation and their interrelation with destination competitiveness, in particular two Travel and Tourism Competitiveness Index (TTCI) pillars, namely, Prioritisation of Travel and Tourism and Price Competitiveness.

The analysis has revealed that restaurants' online reputation is positively influenced by two factors, namely, Core elements, i.e. cooking, service and price-quality ratio, and Price. Furthermore, the restaurants' online reputation does not influence destination competitiveness (TTCI) directly, but indirectly throughout its main constituents, i.e. service and price. Price is the only variable with significant influence on overall TTCI. Within the sample of these destinations, Balkan countries, i.e. Greece and Croatia, perform very well in terms of their restaurants' online reputation. On the other hand, considering the overall TTCI rating, their competitive positions are substantially lower than those of Italy, France and Spain.

The study provides new insights into the relationship between gastronomic offer and destination competitiveness, and valuable practical implications for destination and hospitality management. Moreover, this study addresses various gaps in existing research on this topic. Specifically, it validates the reputation elements presented online using TripAdvisor data and analyses the impact of electronic Word of Mouth (eWOM) not only as the outcome variable of other constructs, as is the case in the literature, but also as a central construct of the analysis. In doing so, it extends current research on this topic and fills the gap regarding the inclusion of the supply-side stakeholder perspective, which has long been recognised as necessary in any attempts to measure competitiveness.

Economic security is one of the crucial dimensions of the welfare state. High-income individuals are able to purchase private insurance, but a large portion of the individuals remains uninsured. The authors have tried to rationalize the problem of the study over the reason why people remain uninsured. Hence, the purpose of the study is to identify an insurance model that can cover the risk of the heterogeneous segments. The study is qualitative in nature and applies a fuzzy analytic hierarchy process (FAHP). Based on seven criteria, process is applied to arrive at an alternative model among basic models of insurance, namely, conventional private insurance, mutual, and social insurance. Since social insurance has emerged with the highest score of 41% in the study, it is implied that social insurance works best in a situation where the market is full of private information and moral hazard. The findings reaffirm that government intervention is required in an insurance market to provide coverage to both covariate and idiosyncratic risks. The findings are especially relevant in the context of emerging markets where a sizeable poor population goes uninsured. The study contributes to the literature by proposing alternative insurance to address the problem of insuring the voluntarily uninsured.