GE models and algorithms for condensed phase equilibrium data regression in ternary systems: limitations and proposals

Phase equilibrium data regression is an unavoidable task necessary to obtain the appropriate values for any model to be used in separation equipment design for chemical process simulation and optimization. The accuracy of this process depends on different factors such as the experimental data quality, the selected model and the calculation algorithm. The present paper summarizes the results and conclusions achieved in our research on the capabilities and limitations of the existing GE models and about strategies that can be included in the correlation algorithms to improve the convergence and avoid inconsistencies. The NRTL model has been selected as a representative local composition model. New capabilities of this model, but also several relevant limitations, have been identified and some examples of the application of a modified NRTL equation have been discussed. Furthermore, a regression algorithm has been developed that allows for the advisable simultaneous regression of all the condensed phase equilibrium regions that are present in ternary systems at constant T and P. It includes specific strategies designed to avoid some of the pitfalls frequently found in commercial regression tools for phase equilibrium calculations. Most of the proposed strategies are based on the geometrical interpretation of the lowest common tangent plane equilibrium criterion, which allows an unambiguous comprehension of the behavior of the mixtures. The paper aims to show all the work as a whole in order to reveal the necessary efforts that must be devoted to overcome the difficulties that still exist in the phase equilibrium data regression problem.

Direct versus indirect channels: Differentiated loss aversion in a high‐involvement, non‐frequently purchased hedonic product

Purpose – This article aims to investigate whether intermediaries reduce loss aversion in the context of a high-involvement non-frequently purchased hedonic product (tourism packages). Design/methodology/approach – The study incorporates the reference-dependent model into a multinomial logit model with random parameters, which controls for heterogeneity and allows representation of different correlation patterns between non-independent alternatives. Findings – Differentiated loss aversion is found: consumers buying high-involvement non-frequently purchased hedonic products are less loss averse when using an intermediary than when dealing with each provider separately and booking their services independently. This result can be taken as identifying consumer-based added value provided by the intermediaries. Practical implications – Knowing the effect of an increase in their prices is crucial for tourism collective brands (e.g. “sun and sea”, “inland”, “green destinations”, “World Heritage destinations”). This is especially applicable nowadays on account of the fact that many destinations have lowered prices to attract tourists (although, in the future, they will have to put prices back up to their normal levels). The negative effect of raising prices can be absorbed more easily via indirect channels when compared to individual providers, as the influence of loss aversion is lower for the former than the latter. The key implication is that intermediaries can – and should – add value in competition with direct e-tailing. Originality/value – Research on loss aversion in retailing has been prolific, exclusively focused on low-involvement and frequently purchased products without distinguishing the direct or indirect character of the distribution channel. However, less is known about other types of products such as high-involvement non-frequently purchased hedonic products. This article focuses on the latter and analyzes different patterns of loss aversion in direct and indirect channels.

Economic crises and tourism competitiveness: A Markov swtiching regression approach

Authors discuss the effects that economic crises generate on the global market shares of tourism destinations, through a series of potential transmission mechanisms based on the main economic competitiveness determinants identified in the previous literature using a non-linear approach. Specifically a Markov Switching Regression approach is used to estimate the effect of two basic transmission mechanisms: reductions of internal and external tourism demands and falling investment.

A demand-driven analysis of tourist accommodation price: A quantile regression of room bookings

Tourist accommodation expenditure is a widely investigated topic as it represents a major contribution to the total tourist expenditure. The identification of the determinant factors is commonly based on supply-driven applications while little research has been made on important travel characteristics. This paper proposes a demand-driven analysis of tourist accommodation price by focusing on data generated from room bookings. The investigation focuses on modeling the relationship between key travel characteristics and the price paid to book the accommodation. To accommodate the distributional characteristics of the expenditure variable, the analysis is based on the estimation of a quantile regression model. The findings support the econometric approach used and enable the elaboration of relevant managerial implications.