El perfil de los consumidores de productos de comercio justo. Análisis de las variables que influyen en su intención de compra

El consumo mundial, impulsor del desarrollo y crecimiento económico de los pueblos, no ha sido igual para todas las naciones, ya que sus efectos han sido diferentes para los ciudadanos de los países del Norte y los del Sur, principalmente por dos razones: una, porque han originado complejos y diferentes estilos de vida y aspiraciones, lo que ha originado grandes diferencias entre los individuos de unos y otros países, y, dos, por su falta de valores sociales y éticos. Ante esta situación, la sociedad en su conjunto debe tomar conciencia de este hecho, y a través de un consumo responsable y de un mayor conocimiento de las relaciones comerciales entre los pueblos, debe optar por consumir productos elaborados bajo criterios de justicia y equidad. Para ayudar a alcanzar estos objetivos de equidad, solidaridad, justicia y ética, nació el Comercio Justo, que, en el caso de España, llegó con veinte años de retraso en la década de los ochenta. Aunque a día de hoy sus ventas crecen a un buen ritmo, siguen siendo inferiores al resto de los países europeos, por cuatro razones: (1) el desconocimiento que la mayoría de los potenciales consumidores tienen de este movimiento social; (2) la dificultad de acceder a los productos que comercializan; (3) el poco impulso que se ofrece desde las Administraciones Públicas; y, (4) porque hay pocas investigaciones en las que se haya analizado el Comercio Justo desde la perspectiva de la demanda, lo que ha implicado que no haya un conocimiento profundo sobre los consumidores de este tipo de productos y sobre sus motivaciones de compra. En base a todo lo expuesto, el presente trabajo se concibe como un estudio exploratorio, que tiene como objetivo principal analizar el perfil de los consumidores y no consumidores de productos de Comercio Justo, sus motivaciones de compra y no compra, así como las variables que influyen en la intención de compra futura, tanto en el segmento de consumidores actuales, como en el segmento de no consumidores de este tipo de productos. Para la realización de este trabajo, se ha utilizado, por una parte, una metodología cualitativa, que ha permitido acceder a la información sobre las opiniones y actitudes que intervienen en los procesos de decisión de compra; y, por otra, una metodología cuantitativa, a través de una encuesta online dirigida a 6.500 individuos, que ha permitido tener información, a través de sendos análisis descriptivos univariante y bivariante, de los individuos encuestados sobre el objeto del estudio. Para validar los modelos y contrastar las hipótesis planteadas, se ha utilizado el análisis de fiabilidad y validación de las escalas de medición seleccionadas (Alpha de Cronbach); el análisis factorial exploratorio, para comprobar la dimensionalidad y validez convergente de las escalas de medida; el análisis factorial confirmatorio, para validar la idoneidad de los modelos de medida propuestos; la regresión logística, para comprobar la validez del modelo general de la probabilidad de la compra o no compra de productos de Comercio Justo; y la regresión lineal múltiple, para comprobar la validez de los modelos específicos de intención de compra futura en los segmentos de compradores y de no compradores. Para realizar todos estos análisis, se han utilizado las herramientas informáticas SPSS v21 y AMOS. Las principales conclusiones del trabajo son: (1) que se deben establecer unos criterios claros que definan quién es quién en el movimiento de Comercio Justo, sus fines, sus objetivos, los productos que comercializan, así como su funcionamiento y desarrollo en España; (2) que, a pesar de las grandes ventajas y posibilidades del Comercio Justo, hay una falta de demanda de estos productos por parte de los consumidores responsables, debido principalmente a la falta de información-comunicación sobre el propio movimiento, y, muy especialmente, a la falta de información sobre los productos, los canales de comercialización, las políticas de precios, las políticas de comunicación, etc., y a la necesidad de que estos productos estén accesibles en los lugares donde los consumidores hacen su compra habitual; y (3) que el Comercio Justo español debe afrontar una serie de desafíos, como son la coordinación entre las diferentes organizaciones que participan en su desarrollo; la sensibilización de los consumidores; la creación de una imagen de marca que defina de una manera clara y sencilla qué es el Comercio Justo; la orientación al cliente y no al producto; y extender la red de comercialización de productos de Comercio Justo a los canales donde los consumidores hacen su compra habitualmente. ABSTRACT Global consumption, the driver of economic growth and development of nations, is not the same for all countries, since its effects have been different on people coming from the North or the South. This is due mainly to two reasons: firstly, because they have developed complex and different lifestyles and aspirations, which have led to significant differences between individuals of one country and another and secondly, because they lack social and ethical values. Given this situation, society as a whole should be aware of this fact, and through responsible consumption and a greater knowledge of trade relations between countries, should opt for consuming products produced with criteria of justice and equity. Fair Trade began as a way to help reach these goals of equity, solidarity, justice and ethics. In the case of Spain it did not start until 20 years later, in the eighties. Although today sales of Fair Trade products are growing at a good rate, they are still below that of other European countries, for four reasons: (1) unawareness of this social movement; (2) the difficult access to these products; (3) insufficient government support; (4) the limited research carried out to analyse Fair Trade from the perspective of demand, resulting in a lack of knowledge about this type of consumer and their purchasing motivations. Based on the above, the present study is designed as an exploratory investigation, aimed at analyzing the profile of consumers and non-consumers of Fair Trade, their motivations for buying and not buying, as well as the variables which influence future purchase intention in both the current consumer segment, and the non-user segment of such products. To carry out this study we have used, on the one hand, a qualitative methodology, to obtain information about the views and attitudes involved in the purchase decision process; and on the other, a quantitative methodology, through an online survey of 6,500 individuals, which provided information through two separate univariate and bivariate descriptive analysis, of the individuals interviewed about the object of this study. To validate the models and contrast hypotheses, we have used the reliability analysis and validation of the selected measurement scales (Cronbach's Alpha); exploratory factor analysis to verify the dimensionality and convergent validity of the measurement scales; confirmatory factor analysis to validate the adequacy of the models of measurement proposed; logistic regression, to verify the validity of the general model of the probability of buying or not buying Fair Trade products; and multiple linear regression to test the validity of specific models in future purchase intention in the segments of buyers and non-buyers. To carry out these analyses, we used SPSS v21 software tools and AMOS. The principal conclusions of the investigation are: (1) the need to establish clear criteria which define who is who in the Fair Trade movement, its goals, objectives, the products they sell, as well as its operation and development in Spain; (2) that despite the great advantages and possibilities of Fair Trade, there is a lack of demand for these products by responsible consumers, mainly due to the lack of information-communication about the movement itself, and especially on the range of products, sales channels, pricing policies, communication policies, etc., and the need for these products to be available in places where consumers make their usual purchase; and (3) that Spanish Fair Trade must address a number of challenges such as: coordination between the different organizations involved in trade development; consumer awareness; creation of a brand image that defines in a clear and simple way what Fair Trade is; focus on the customer rather than the product; and expansion of the network of Fair Trade sales outlets to include the channels where consumers usually make their purchases.

Physical properties of sediment core CRP-1

The relationship between whole-core compressional wave velocities and gamma-ray attenuation porosities of sediments cored at CRP-1 is examined and compared with results from core-plug samples and global models. Both core-plug and whole-core velocities show a strong dependence on porosity: this relationship appears to be independent of lithology. In the range from 0.1 to 0.4 of fractional porosity (Miocene strata), plug velocities are generally 0.2 - 0.5 km s-1 higher than whole-core velocities. Possible reasons include decreased rigidity in the whole core and diagenetic changes in the plugs. Possibly both velocity measurements are correct but neither is fully representative for in situ conditions. It appears that the core-plug results are more compatible with data from other regions than the whole-core data. After removing first-order compaction control from the whole-core porosity record, a second-order control by clay content can be quantified as a simple positive linear regression (R=0.6). In contrast, after correction for first-order control, porosity and velocity are not significantly influenced by lonestone abundance except for rare, very large lonestones.

Effects of rock properties on specific cutting energy in linear cutting of sandstones by picks

Specific cutting energy (SE) has been widely used to assess the rock cuttability for mechanical excavation purposes. Some prediction models were developed for SE through correlating rock properties with SE values. However, some of the textural and compositional rock parameters i.e. texture coefficient and feldspar, mafic, and felsic mineral contents were not considered. The present study is to investigate the effects of previously ignored rock parameters along with engineering rock properties on SE. Mineralogical and petrographic analyses, rock mechanics, and linear rock cutting tests were performed on sandstone samples taken from sites around Ankara, Turkey. Relationships between SE and rock properties were evaluated using bivariate correlation and linear regression analyses. The tests and subsequent analyses revealed that the texture coefficient and feldspar content of sandstones affected rock cuttability, evidenced by significant correlations between these parameters and SE at a 90% confidence level. Felsic and mafic mineral contents of sandstones did not exhibit any statistically significant correlation against SE. Cementation coefficient, effective porosity, and pore volume had good correlations against SE. Poisson's ratio, Brazilian tensile strength, Shore scleroscope hardness, Schmidt hammer hardness, dry density, and point load strength index showed very strong linear correlations against SE at confidence levels of 95% and above, all of which were also found suitable to be used in predicting SE individually, depending on the results of regression analysis, ANOVA, Student's t-tests, and R2 values. Poisson's ratio exhibited the highest correlation with SE and seemed to be the most reliable SE prediction tool in sandstones.

Effects of rock properties on specific cutting energy in linear cutting of sandstones by picks

Specific cutting energy (SE) has been widely used to assess the rock cuttability for mechanical excavation purposes. Some prediction models were developed for SE through correlating rock properties with SE values. However, some of the textural and compositional rock parameters i.e. texture coefficient and feldspar, mafic, and felsic mineral contents were not considered. The present study is to investigate the effects of previously ignored rock parameters along with engineering rock properties on SE. Mineralogical and petrographic analyses, rock mechanics, and linear rock cutting tests were performed on sandstone samples taken from sites around Ankara, Turkey. Relationships between SE and rock properties were evaluated using bivariate correlation and linear regression analyses. The tests and subsequent analyses revealed that the texture coefficient and feldspar content of sandstones affected rock cuttability, evidenced by significant correlations between these parameters and SE at a 90% confidence level. Felsic and mafic mineral contents of sandstones did not exhibit any statistically significant correlation against SE. Cementation coefficient, effective porosity, and pore volume had good correlations against SE. Poisson's ratio, Brazilian tensile strength, Shore scleroscope hardness, Schmidt hammer hardness, dry density, and point load strength index showed very strong linear correlations against SE at confidence levels of 95% and above, all of which were also found suitable to be used in predicting SE individually, depending on the results of regression analysis, ANOVA, Student's t-tests, and R-2 values. Poisson's ratio exhibited the highest correlation with SE and seemed to be the most reliable SE prediction tool in sandstones.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Structural change in macroeconomic time series: A complex systems perspective

We demonstrate that the process of generating smooth transitions Call be viewed as a natural result of the filtering operations implied in the generation of discrete-time series observations from the sampling of data from an underlying continuous time process that has undergone a process of structural change. In order to focus discussion, we utilize the problem of estimating the location of abrupt shifts in some simple time series models. This approach will permit its to address salient issues relating to distortions induced by the inherent aggregation associated with discrete-time sampling of continuous time processes experiencing structural change, We also address the issue of how time irreversible structures may be generated within the smooth transition processes. (c) 2005 Elsevier Inc. All rights reserved.

The use of correlation and regression methods in optometry

Correlation and regression are two of the statistical procedures most widely used by optometrists. However, these tests are often misused or interpreted incorrectly, leading to erroneous conclusions from clinical experiments. This review examines the major statistical tests concerned with correlation and regression that are most likely to arise in clinical investigations in optometry. First, the use, interpretation and limitations of Pearson's product moment correlation coefficient are described. Second, the least squares method of fitting a linear regression to data and for testing how well a regression line fits the data are described. Third, the problems of using linear regression methods in observational studies, if there are errors associated in measuring the independent variable and for predicting a new value of Y for a given X, are discussed. Finally, methods for testing whether a non-linear relationship provides a better fit to the data and for comparing two or more regression lines are considered.

Statnote 24: Multiple regression

In previous statnotes, the application of correlation and regression methods to the analysis of two variables (X,Y) was described. These methods can be used to determine whether there is a linear relationship between the two variables, whether the relationship is positive or negative, to test the degree of significance of the linear relationship, and to obtain an equation relating Y to X. This Statnote extends the methods of linear correlation and regression to situations where there are two or more X variables, i.e., 'multiple linear regression’.

Regression analysis of ranked segment parameters for optic nerve head classification:a pilot study

Purpose: To determine whether curve-fitting analysis of the ranked segment distributions of topographic optic nerve head (ONH) parameters, derived using the Heidelberg Retina Tomograph (HRT), provide a more effective statistical descriptor to differentiate the normal from the glaucomatous ONH. Methods: The sample comprised of 22 normal control subjects (mean age 66.9 years; S.D. 7.8) and 22 glaucoma patients (mean age 72.1 years; S.D. 6.9) confirmed by reproducible visual field defects on the Humphrey Field Analyser. Three 10°-images of the ONH were obtained using the HRT. The mean topography image was determined and the HRT software was used to calculate the rim volume, rim area to disc area ratio, normalised rim area to disc area ratio and retinal nerve fibre cross-sectional area for each patient at 10°-sectoral intervals. The values were ranked in descending order, and each ranked-segment curve of ordered values was fitted using the least squares method. Results: There was no difference in disc area between the groups. The group mean cup-disc area ratio was significantly lower in the normal group (0.204 ± 0.16) compared with the glaucoma group (0.533 ± 0.083) (p < 0.001). The visual field indices, mean deviation and corrected pattern S.D., were significantly greater (p < 0.001) in the glaucoma group (-9.09 dB ± 3.3 and 7.91 ± 3.4, respectively) compared with the normal group (-0.15 dB ± 0.9 and 0.95 dB ± 0.8, respectively). Univariate linear regression provided the best overall fit to the ranked segment data. The equation parameters of the regression line manually applied to the normalised rim area-disc area and the rim area-disc area ratio data, correctly classified 100% of normal subjects and glaucoma patients. In this study sample, the regression analysis of ranked segment parameters method was more effective than conventional ranked segment analysis, in which glaucoma patients were misclassified in approximately 50% of cases. Further investigation in larger samples will enable the calculation of confidence intervals for normality. These reference standards will then need to be investigated for an independent sample to fully validate the technique. Conclusions: Using a curve-fitting approach to fit ranked segment curves retains information relating to the topographic nature of neural loss. Such methodology appears to overcome some of the deficiencies of conventional ranked segment analysis, and subject to validation in larger scale studies, may potentially be of clinical utility for detecting and monitoring glaucomatous damage. © 2007 The College of Optometrists.

Analysing Conjoint Analysis Data by a Random Coefficient Regression Model

2000 Mathematics Subject Classification: 62J12, 62K15, 91B42, 62H99.

On the Lp-Norm Regression Models for Estimating Value-at-Risk

Analysis of risk measures associated with price series data movements and its predictions are of strategic importance in the financial markets as well as to policy makers in particular for short- and longterm planning for setting up economic growth targets. For example, oilprice risk-management focuses primarily on when and how an organization can best prevent the costly exposure to price risk. Value-at-Risk (VaR) is the commonly practised instrument to measure risk and is evaluated by analysing the negative/positive tail of the probability distributions of the returns (profit or loss). In modelling applications, least-squares estimation (LSE)-based linear regression models are often employed for modeling and analyzing correlated data. These linear models are optimal and perform relatively well under conditions such as errors following normal or approximately normal distributions, being free of large size outliers and satisfying the Gauss-Markov assumptions. However, often in practical situations, the LSE-based linear regression models fail to provide optimal results, for instance, in non-Gaussian situations especially when the errors follow distributions with fat tails and error terms possess a finite variance. This is the situation in case of risk analysis which involves analyzing tail distributions. Thus, applications of the LSE-based regression models may be questioned for appropriateness and may have limited applicability. We have carried out the risk analysis of Iranian crude oil price data based on the Lp-norm regression models and have noted that the LSE-based models do not always perform the best. We discuss results from the L1, L2 and L∞-norm based linear regression models. ACM Computing Classification System (1998): B.1.2, F.1.3, F.2.3, G.3, J.2.

Classification of Texts' Authorship Using a Regression Model on Compressed Data

2010 Mathematics Subject Classification: 68T50,62H30,62J05.

The use of Poisson regression in the sociological study of suicide

This paper explains how Poisson regression can be used in studies in which the dependent variable describes the number of occurrences of some rare event such as suicide. After pointing out why ordinary linear regression is inappropriate for treating dependent variables of this sort, we go on to present the basic Poisson regression model and show how it fits in the broad class of generalized linear models. Then we turn to discussing a major problem of Poisson regression known as overdispersion and suggest possible solutions, including the correction of standard errors and negative binomial regression. The paper ends with a detailed empirical example, drawn from our own research on suicide.

Development of operational performance models for truck-lane restrictions on freeway corridors

Highways are generally designed to serve a mixed traffic flow that consists of passenger cars, trucks, buses, recreational vehicles, etc. The fact that the impacts of these different vehicle types are not uniform creates problems in highway operations and safety. A common approach to reducing the impacts of truck traffic on freeways has been to restrict trucks to certain lane(s) to minimize the interaction between trucks and other vehicles and to compensate for their differences in operational characteristics. ^ The performance of different truck lane restriction alternatives differs under different traffic and geometric conditions. Thus, a good estimate of the operational performance of different truck lane restriction alternatives under prevailing conditions is needed to help make informed decisions on truck lane restriction alternatives. This study develops operational performance models that can be applied to help identify the most operationally efficient truck lane restriction alternative on a freeway under prevailing conditions. The operational performance measures examined in this study include average speed, throughput, speed difference, and lane changes. Prevailing conditions include number of lanes, interchange density, free-flow speeds, volumes, truck percentages, and ramp volumes. ^ Recognizing the difficulty of collecting sufficient data for an empirical modeling procedure that involves a high number of variables, the simulation approach was used to estimate the performance values for various truck lane restriction alternatives under various scenarios. Both the CORSIM and VISSIM simulation models were examined for their ability to model truck lane restrictions. Due to a major problem found in the CORSIM model for truck lane modeling, the VISSIM model was adopted as the simulator for this study. ^ The VISSIM model was calibrated mainly to replicate the capacity given in the 2000 Highway Capacity Manual (HCM) for various free-flow speeds under the ideal basic freeway section conditions. Non-linear regression models for average speed, throughput, average number of lane changes, and speed difference between the lane groups were developed. Based on the performance models developed, a simple decision procedure was recommended to select the desired truck lane restriction alternative for prevailing conditions. ^

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^