Price sensitivity to tourism activities: looking for determinant factors

The literature contains evidence that there is a marked heterogeneity in price responses to tourism products, leading to a great variety of tourist sensitivities to price. Thus the role price plays is complex, and a particularly challenging aspect of this complexity is that its effect is not unambiguous, thereby negating the idea that the demand for tourism products and tourist activities can always be regarded as demand for ordinary goods. This article identifies and explains, as a novelty for the tourism industry, price sensitivities to tourism activities individual by individual. The operative formalization uses a mixed logit model to estimate the individual sensitivities to price, and then a regression analysis is applied to detect their determinants. The empirical application finds that motivations, influenced by age, and length of stay with a non-linear effect, are explanatory factors of tourists’ price sensitivity to activities.

Who performs a stronger response to whom? Detecting individual competitive actions and reactions

This study analyzes the degree of competition through individual actions and reactions. Empirical support for this analysis has derived mainly from structural econometric models describing the nature of competition. This analysis extends the existing literature by empirically considering a direct measurement of competition through the analysis of the competitive actions and responses, and describing how firms compete within and between strategic groups. We estimate the firms’ conduct in the Spanish deposits market with 146 firms and 18,888 observations. This is a specially compelling context for the banking industry, in which a deregulation process gives rise to the adoption of aggressive strategies seeking to increase the market shares of deposit accounts; thus, producing a turbulent situation of increasing rivalry. Our results offer a deeper understanding of the firms’ competitive behavior, since we identify different patterns of actions and reactions depending upon the strategic group the firm belongs to.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Análisis de la percepción de seguridad en puentes peatonales: una aproximación mediante modelación híbrida

Este artículo de investigación científica y tecnológica estudia la percepción de seguridad en el uso de puentes peatonales, empleando un enfoque sustentado en dos campos principales: el microeconómico y el psicológico. El trabajo hace la estimación simultánea de un modelo híbrido de elección y variables latentes con datos de una encuesta de preferencias declaradas, encontrando mejor ajuste que un modelo mixto de referencia, lo que indica que la percepción de seguridad determina el comportamiento de los peatones cuando se enfrentan a la decisión de usar o no un puente peatonal. Se encontró que el sexo, la edad y el nivel de estudios son atributos que inciden en la percepción de seguridad. El modelo calibrado sugiere varias estrategias para aumentar el uso de puentes peatonales que son discutidas, encontrando que el uso de barreras ocasiona una pérdida de utilidad, en los peatones, que debería ser estudiada como extensión del presente trabajo.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Detecting Free Riders in Collective Brands through a Hierarchical Choice Process

Free-riding behaviors exist in tourism and they should be analyzed from a comprehensive perspective; while the literature has mainly focused on free riders operating in a destination, the destinations themselves might also free ride when they are under the umbrella of a collective brand. The objective of this article is to detect potential free-riding destinations by estimating the contribution of the different individual destinations to their collective brands, from the point of view of consumer perception. We argue that these individual contributions can be better understood by reflecting the various stages that tourists follow to reach their final decision. A hierarchical choice process is proposed in which the following choices are nested (not independent): “whether to buy,” “what collective brand to buy,” and “what individual brand to buy.” A Mixed Logit model confirms this sequence, which permits estimation of individual contributions and detection of free riders.

Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h²) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (ĥ²) were compared with h² using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean ĥ² values were under- and overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering ĥ², probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.

Mixed rain forest in southeastern Brazil: tree species regeneration and floristic relationships in a remaining stretch of forest near the city of Itaberá, Brazil

The aim of this work was to evaluate the floristic composition, richness, and diversity of the upper and lower strata of a stretch of mixed rain forest near the city of Itaberá, in southeastern Brazil. We also investigated the differences between this conservation area and other stretches of mixed rain forest in southern and southeastern Brazil, as well as other nearby forest formations, in terms of their floristic relationships. For our survey of the upper stratum (diameter at breast height [DBH] > 15 cm), we established 50 permanent plots of 10 × 20 m. Within each of those plots, we designated five, randomly located, 1 × 1 m subplots, in order to survey the lower stratum (total height > 30 cm and DBH < 15 cm). In the upper stratum, we sampled 1429 trees and shrubs, belonging to 134 species, 93 genera, and 47 families. In the lower stratum, we sampled 758 trees and shrubs, belonging to 93 species, 66 genera, and 39 families. In our floristic and phytosociological surveys, we recorded 177 species, belonging to 106 genera and 52 families. The Shannon Diversity Index was 4.12 and 3.5 for the upper and lower strata, respectively. Cluster analysis indicated that nearby forest formations had the strongest floristic influence on the study area, which was therefore distinct from other mixed rain forests in southern Brazil and in the Serra da Mantiqueira mountain range.

Influence of separate and mixed experimental designs on reaction times to two simple visual stimuli

Testing contexts have been shown to critically influence experimental results in psychophysical studies. One of these contexts that show important modulation of the behavioral effects of different stimulatory conditions is the separate (blocked) or mixed presentation of these stimulatory conditions. The study presents evidence that the apparent discriminabilities of two target stimuli can change according to which of these two testing contexts is used. A cross inside a ring and a vertical line inside a ring were presented as go stimuli in a go/no-go reaction time task. In one experiment, each of these stimuli was presented to a different group of volunteers and in another experiment they were presented to the same group of volunteers, randomly mixed in the blocks of trials. Similar reaction times were obtained for the two stimuli in the first experiment, and different reaction times (faster for the cross) in the second experiment. The latter result indicates that the two stimuli have different discriminabilities from the no-go stimulus; the cross having greater discriminability. This difference is however masked, presumably by the adoption of specific compensatory attentional sets, in a separate testing context.

The effect of using mixed initiator systems on the efficiency of photopolymerization of dental resins

A study was performed in order to determine the efficiency of the simultaneous use of the photoinitiators phenylpropanedione (PPD) and camphorquinone (CQ) in the polymerization of acrylic polymers and evaluate possible mechanisms leading to synergism or antagonism. It was found that efficiencies of both initiators taken individually are higher than that of their mixture, indicating that when both dyes are used simultaneously there will be an energy transfer from the more efficient initiator (CQ) to the less efficient one (PPD). Also, there was no proof of any reaction between the amine present in the CQ formulation and the PPD excited state.

A mixed colony of Scaptotrigona depilis and Nannotrigona testaceicornis (Hymenoptera, Apidae, Meliponina)

We describe a case of a spontaneously established mixed colony of two species of stingless bees. The host colony of Scaptotrigona depilis, an aggressive bee that forms large colonies, was invaded by workers of Nannotrigona testaceicornis, a smaller bee that forms small colonies. The host colony and the invading species colony were maintained in next boxes about 1.5 m apart. The N. testaceicornis colony had been recently divided. Observations were made daily for 10 min, and every two weeks the colony was opened for observations within the nest. Initially the host colony bees repulsed the invading species, but as their numbers built up, they were no longer able to defend the entrance. An estimated 60-90 N. testaceicornis workers lived integrated into the colony of S. depilis for 58 days. During this period, they reconstructed and maintained the entrance tube, changing it to an entrance typical of N. testaceicornis. They also collected food and building material for the host colony. Nannotrigona testaceicornis tolerated transit of S. depilis through the entrance, but did not allow the host species to remain within the tube, though the attacks never resulted in bee mortality. Aggression was limited to biting the wings; when the bees fell to the ground they immediately separated and flew back. There have been very few reports of spontaneously occurring mixed stingless bee colonies. It is difficult to determine what caused the association that we found; probably workers of N. testaceicornis got lost when we split their colony, and then they invaded the colony of S. depilis.

Radiative decay of the X(3872) as a mixed molecule-charmonium state in QCD sum rules

We use QCD sum rules (QCDSR) to calculate the width of the radiative decay of the meson X(3872), assumed to be a mixture between charmonium and exotic molecular [c (q) over bar][q (c) over bar] states with J(PC) = 1(++). We find that in a small range for the values of the mixing angle, 5 degrees <= theta <= 13 degrees, we get the branching ratio Gamma(X -> J/psi gamma)/Gamma(X -> J/psi pi(+)pi(-)) = 0.19 +/- 0.13, which is in agreement, with the experimental value. This result is compatible with the analysis of the mass and decay width of the mode J/psi(n pi) performed in the same approach.

QCD sum rules for the X(3872) as a mixed molecule-charmonium state

We use QCD sum rules to test the nature of the meson X(3872), assumed to be a mixture between charmonium and exotic molecular [c (q) over bar][q (c) over bar] states with J(PC) = 1(++). We find that there is only a small range for the values of the mixing angle theta that can provide simultaneously good agreement with the experimental value of the mass and the decay width, and this range is 5(0) <= theta <= 3(0). In this range we get m(X) = (3.77 +/- 0.18) GeV and Gamma(X -> J/psi pi(+)pi(-)) = (9.3 +/- 6.9) MeV, which are compatible, within the errors, with the experimental values. We, therefore, conclude that the X(3872) is approximately 97% a charmonium state with 3% admixture of similar to 88% D(0)D*(0) molecule and similar to 12% D(+)D*(-) molecule.

Detection of catechol using mixed Langmuir-Blodgett films of a phospholipid and phthalocyanines as voltammetric sensors

The combination of metallic phthalocyanines (MPcs) and biomolecules has been explored in the literature either as mimetic systems to investigate molecular interactions or as supporting layers to immobilize biomolecules. Here, Langmuir-Blodgett (LB) films containing the phospholipid dimyristoyl phosphatidic acid (DMPA) mixed either with iron phthalocyanine (FePc) or with lutetium bisphthalocyanine (LuPc(2)) were applied as ITO modified-electrodes in the detection of catechol using cyclic voltammetry. The mixed Langmuir films of FePc + DMPA and LuPc(2) + DMPA displayed surface-pressure isotherms with no evidence of molecular-level interactions. The Fourier Transform Infrared (FTIR) spectra of the multilayer LB films confirmed the lack of interaction between the components. The DMPA and the FePc molecules were found to be oriented perpendicularly to the substrate, while LuPc(2) molecules were randomly organized. The phospholipid matrix induced a remarkable electrocatalytic effect on the phthalocyanines; as a result the mixed LB films deposited on ITO could be used to detect catechol with detection limits of 4.30 x 10(-7) and 3.34 x 10(-7) M for FePc + DMPA and LuPc(2) + DMPA, respectively. Results from kinetics experiments revealed that ion diffusion dominated the response of the modified electrodes. The sensitivity was comparable to that of other non-enzymatic sensors, which is sufficient to detect catechol in the food industry. The higher stability of the electrochemical response of the LB films and the ability to control the molecular architecture are promising for further studies with incorporation of biomolecules.