Search results

The purpose of this paper is to examine, through a microeconomic analysis, the extent to which trip characteristics influence tourism expenditure in 14 emerging urban-cultural cities in Andalusia (Spain).

This analysis was carried out using an ordinary least squares method, which measures influence on tourism expenditure based on the trip characteristics of tourists visiting emerging urban-cultural cities. For this, the authors used 3,030 surveys conducted on tourists who, in 2013, visited 14 emerging urban-cultural cities in Andalusia (Spain).

It was confirmed that certain trip characteristics – type of accommodation, length of stay, trip planning and internet use – determine tourism expenditure in these destinations. The findings provide stakeholders in these destinations with information for the implementation of policies aiming to increase revenue in destinations where tourism development levels are still in their infancy and where, therefore, there are many unexploited opportunities.

First, this study identifies those trip characteristics which influence tourist expenditure in emerging urban tourist destinations. These destinations, to date, had not been previously analysed in expenditure segmentation studies. Second, aside from the factors traditionally analysed in scientific literature, other trip-specific variables were considered; these relate to the means by which tourists familiarise themselves with their destination and the way in which they plan their trip (use of the internet), as the rise of new technologies has radically changed tourism.

This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.

Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.

The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.

The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations.

The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

The purpose of this paper is to analyze the changes in wealth and consumption inequality in Spain and estimate the consumption effects of housing and financial wealth.

The estimations are made using micro-data from the Spanish Survey of Household Finances (2002–2014) applying cross-section, panel and interquartile techniques.

The findings of this paper suggest that there was an increase in wealth inequality during the period under analysis and a reduction in consumption inequality. Also, the authors find a significant positive effect of wealth on consumer expenditure. Disaggregating by asset type, the value of the main residence is the category with the highest estimated effect on consumption, whereas the remaining types of assets, although still positive and generally significant, have more modest effects on consumption. However, the estimated coefficients and their significance can change substantially depending on the phase of the economic cycle and the position of the household in the income distribution.

These results provide new empirical evidence on the effects of household wealth changes on their consumption behavior, the differences depending on the household's position in the distribution and the fluctuations of these estimated coefficients throughout a period of profound economic upheavals.

This paper aims to investigate the fatigue life and dynamic loads of an arresting cable when a landing aircraft passes through a runway.

The cable is assumed to be under tension and with bending rigidity. The normal modes and natural frequencies of the vibrating wire are obtained analytically. The response of the arresting cable owing to the impact of the landing aircraft is modelled as a problem of initial conditions. The maximum normal and tangential stresses are determined for different landing conditions and landing aircraft sizes. The fatigue life of the wire is determined based on classical theories.

The results show that maximum shear stresses occur at the end of the cable. These shear stresses increased with higher off-centre landing position and decreased tyre width. Between 50 and 70% of the service life of the cable is reduced due to the dynamic loading effects.

A novel technique based on the solution of a continuous elastic system is proposed for analysing the displacements and stresses on an arresting cable due to the impact of a landing aircraft. The derived stresses can be used to calculate the service life of the cable.

This paper aims to study, by means of an empirical approach, how monetary policy might affect the distribution of individual income.

After describing the channels through which monetary policy could impinge on income distribution, the authors carry out a panel analysis of 62 countries that control their monetary policy for the period 1996–2015.

Using two possible proxy variables for monetary policy (the monetary aggregate M3 and the real interest rates), the results reveal a significant positive relationship between real interest rates and income inequality measured through the market Gini coefficient and polarization ratios. The findings suggest that central bankers should be more aware of the redistributive effects of monetary policy.

It should be mentioned the major challenge of data limitation in the empirical investigation on the relationship between monetary policies and inequalities.

The empirical evidence presented in this paper supports the premise that central bankers should not ignore the unintended redistributive consequences of their actions. In this regard, it is worth noting that if, in addition to price stability, central banks are also responsible for financial stability; the rationale behind central bank independence needs to be reconsidered.

An outstanding feature of the paper is its sample size and the variety of countries included in the sample, which includes countries from all continents and with very different levels of economic development. Also, unlike papers based on forecasting modeling – e.g. Vector autoregression (VAR) or Structural vector autoregression (SVAR) models, the study follows an explanatory approach, including not only monetary variables, but also a series of regressors that may have a meaningful and significant impact on inequality, according to a wide literature.

The use of the 4D trajectory operational concept in the future air traffic management (ATM) system will require the aircraft to meet very accurately an arrival time over a designated checkpoint. To do this, time intervals known as time windows (TW) are defined. The purpose of this paper is to develop a methodology to characterise these TWs and to manage the uncertainty associated with the evolution of 4D trajectories.

4D trajectories are modelled using a point mass model and EUROCONTROL’s BADA methodology. The authors stochastically evaluate the variability of the parameters that influence 4D trajectories using Monte Carlo simulation. This enables the authors to delimit TWs for several checkpoints. Finally, the authors set out a causal model, based on a Bayesian network approach, to evaluate the impact of variations in fundamental parameters at the chosen checkpoints.

The initial results show that the proposed TW model limits the deviation in time to less than 27 s at the checkpoints of an en-route segment (300 NM).

The objective of new trajectory-based operations is to efficiently and strategically manage the expected increase in air traffic volumes and to apply tactical interventions as a last resort only. We need new tools to support 4D trajectory management functions such as strategic and collaborative planning. The authors propose a novel approach for to ensure aircraft punctuality.

The main contribution of the paper is the development of a model to deal with uncertainty and to increase predictability in 4D trajectories, which are key elements of the future airspace operational environment.

Cryptocurrencies are notoriously difficult to value from a fundamental perspective. This valuation challenge is rooted in various debated issues in academia and the investments industry. For example, do cryptocurrencies and other cryptoassets have intrinsic value in the conventional sense? Can one appropriately regard cryptocurrencies as digital fiat currencies? What distinguishes cryptocurrencies such as bitcoin and ether from precious metals like gold from a financial perspective? How do cryptocurrencies compare to other cryptoassets in terms of pricing and valuation? This chapter aims to provide responses to these questions, discuss approaches to cryptoasset valuation, and identify areas for future research.

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.