Shopping motives as antecedents of e-satisfaction and e-loyalty

Customer loyalty is fundamental to the profitability and survival of e-tailers. Yet research on antecedents of e-loyalty is relatively limited. This study contributes to the literature by investigating the effect of motives for online shopping on e-satisfaction and e-loyalty. A structural equations model is developed and tested through data from an online survey involving 797 customers of two UK-based e-tailers focussing on hedonic products. The results suggest that convenience, variety seeking, and social interaction help predict e-satisfaction, and that social interaction is the only shopping motive examined with a direct relationship to e-loyalty. Data also show that e-satisfaction is a strong determinant of e-loyalty. These findings are discussed in the light of previous research and avenues of future research are proposed.

Risk perception and commitment to reduce global climate change in Spain

An online national survey among the Spanish population (n = 602) was conducted to examine the factors underlying a person’s support for commitments to global climate change reductions. Multiple hierarchical regression analysis was conducted in four steps and a structural equations model was tested. A survey tool designed by the Yale Project on Climate Change Communication was applied in order to build scales for the variables introduced in the study. The results show that perceived consumer effectiveness and risk perception are determinant factors of commitment to mitigating global climate change. However, there are differences in the influence that other factors, such as socio-demographics, view of nature and cultural cognition, have on the last predicted variable.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering or consensus based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ - a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilising the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from: http://www.reading.ac.uk/bioinf/downloads/ CONTACT: l.j.mcguffin@reading.ac.uk.

Simple general method of generating non-oxo, non-amavadine model octacoordinated vanadium(IV) complexes of some tetradentate ONNO chelating ligands from various oxovanadium(IV/V) compounds and structural characterization of one of them

A simple general route of obtaining very stable octacoordinated non-oxovanadium( IV) complexes of the general formula VL2 (where H2L is a tetradentate ONNO donor) is presented. Six such complexes (1-6) are adequately characterized by elemental analysis, mass spectrometry, and various spectroscopic techniques. One of these compounds (1) has been structurally characterized. The molecule has crystallographic 4 symmetry and has a dodecahedral structure existing in a tetragonal space group P4n2. The non-oxo character and VL2 stoichiometry for all of the complexes are established from analytical and mass spectrometric data. In addition, the non-oxo character is clearly indicated by the complete absence of the strong nu(v=o) band in the 925-1025 cm(-1) region, which is a signature of all oxovanadium species. The complexes are quite stable in open air in the solid state and in solution, a phenomenon rarely observed in non-oxovanadium(IV) or bare vanadium(IV) complexes.

Bayesian model averaging and identification of structural breaks in time series

Bayesian Model Averaging (BMA) is used for testing for multiple break points in univariate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over specifications including: stationary; stationary around trend and unit root models, each containing different types and number of breaks and different lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in all of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our findings regarding the existence of unit roots, having allowed for structural breaks in the data, are largely consistent with previous work.

Risk taking for oneself and others: a structural model approach

Economic theory makes no predictions about social factors affecting decisions under risk. We examine situations in which a decision maker decides for herself and another person under conditions of payoff equality, and compare them to individual decisions. By estimating a structural model, we find that responsibility leaves utility curvature unaffected, but accentuates the subjective distortion of very small and very large probabilities for both gains and losses. We also find that responsibility reduces loss aversion, but that these results only obtain under some specific definitions of the latter. These results serve to generalize and reconcile some of the still largely contradictory findings in the literature. They also have implications for financial agency, which we discuss.

Comparison of stochastic parametrization approaches in a single-column model

We discuss and test the potential usefulness of single-column models (SCMs) for the testing of stchastic physics schemes that have been proposed for use in general circulation models (GCMs). We argue that although single column tests cannot be definitive in exposing the full behaviour of a stochastic method in the full GCM, and although there are differences between SCM testing of deterministic and stochastic methods, nonetheless SCM testing remains a useful tool. It is necessary to consider an ensemble of SCM runs produced by the stochastic method. These can be usefully compared to deterministic ensembles describing initial condition uncertainty and also to combinations of these (with structural model changes) into poor man's ensembles. The proposed methodology is demonstrated using an SCM experiment recently developed by the GCSS community, simulating the transitions between active and suppressed periods of tropical convection.

Comparison of stochastic parameterisation approaches in a single-column model

We discuss and test the potential usefulness of single-column models (SCMs) for the testing of stochastic physics schemes that have been proposed for use in general circulation models (GCMs). We argue that although single column tests cannot be definitive in exposing the full behaviour of a stochastic method in the full GCM, and although there are differences between SCM testing of deterministic and stochastic methods, SCM testing remains a useful tool. It is necessary to consider an ensemble of SCM runs produced by the stochastic method. These can be usefully compared to deterministic ensembles describing initial condition uncertainty and also to combinations of these (with structural model changes) into poor man's ensembles. The proposed methodology is demonstrated using an SCM experiment recently developed by the GCSS (GEWEX Cloud System Study) community, simulating transitions between active and suppressed periods of tropical convection.

A structural time series approach to modelling multiple and resurgent meat scares in Italy

This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.

A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms

Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree.

The ModFOLD server for the quality assessment of protein structural models

The reliable assessment of the quality of protein structural models is fundamental to the progress of structural bioinformatics. The ModFOLD server provides access to two accurate techniques for the global and local prediction of the quality of 3D models of proteins. Firstly ModFOLD, which is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. Secondly ModFOLDclust, which is a more intensive method that carries out clustering of multiple models and provides per-residue local quality assessment.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

On the structural differences between markers and genomic AC microsatellites

AC microsatellites have proved particularly useful as genetic markers. For some purposes, such as in population biology, the inferences drawn depend on the quantitative values of their mutation rates. This, together with intrinsic biological interest, has led to widespread study of microsatellite mutational mechanisms. Now, however, inconsistencies are appearing in the results of marker-based versus non-marker-based studies of mutational mechanisms. The reasons for this have not been investigated, but one possibility, pursued here, is that the differences result from structural differences between markers and genomic microsatellites. Here we report a comparison between the CEPH AC marker microsatellites and the global population of AC microsatellites in the human genome. AC marker microsatellites are longer than the global average. Controlling for length, marker microsatellites contain on average fewer interruptions, and have longer segments, than their genomic counterparts. Related to this, marker microsatellites show a greater tendency to concentrate the majority of their repeats into one segment. These differences plausibly result from scientists selecting markers for their high polymorphism. In addition to the structural differences, there are differences in the base composition of flanking sequences, marker flanking regions being richer in C and G and poorer in A and T. Our results indicate that there are profound differences between marker and genomic microsatellites that almost certainly affect their mutation rates. There is a need for a unified model of mutational mechanisms that accounts for both marker-derived and genomic observations. A suggestion is made as to how this might be done.

First structural analysis of a naphthalene-based poly(ether ketone): Crystal and molecular simulation from X-ray powder data and diffraction modeling

Polycondensation of 2,6-dihydroxynaphthalene with 4,4'-bis(4"-fluorobenzoyl)biphenyl affords a novel, semicrystalline poly(ether ketone) with a melting point of 406 degreesC and glass transition temperature (onset) of 168 degreesC. Molecular modeling and diffraction-simulation studies of this polymer, coupled with data from the single-crystal structure of an oligomer model, have enabled the crystal and molecular structure of the polymer to be determined from X-ray powder data. This structure-the first for any naphthalene-containing poly(ether ketone)-is fully ordered, in monoclinic space group P2(1)/b, with two chains per unit cell. Rietveld refinement against the experimental powder data gave a final agreement factor (R-wp) of 6.7%.

Low-molecular-weight gelators: Elucidating the principles of gelation based on gelator solubility and a cooperative self-assembly model

This paper highlights the key role played by solubility in influencing gelation and demonstrates that many facets of the gelation process depend on this vital parameter. In particular, we relate thermal stability (T-gel) and minimum gelation concentration (MGC) values of small-molecule gelation in terms of the solubility and cooperative self-assembly of gelator building blocks. By employing a van't Hoff analysis of solubility data, determined from simple NMR measurements, we are able to generate T-calc values that reflect the calculated temperature for complete solubilization of the networked gelator. The concentration dependence of T-calc allows the previously difficult to rationalize "plateau-region" thermal stability values to be elucidated in terms of gelator molecular design. This is demonstrated for a family of four gelators with lysine units attached to each end of an aliphatic diamine, with different peripheral groups (Z or Bee) in different locations on the periphery of the molecule. By tuning the peripheral protecting groups of the gelators, the solubility of the system is modified, which in turn controls the saturation point of the system and hence controls the concentration at which network formation takes place. We report that the critical concentration (C-crit) of gelator incorporated into the solid-phase sample-spanning network within the gel is invariant of gelator structural design. However, because some systems have higher solubilities, they are less effective gelators and require the application of higher total concentrations to achieve gelation, hence shedding light on the role of the MGC parameter in gelation. Furthermore, gelator structural design also modulates the level of cooperative self-assembly through solubility effects, as determined by applying a cooperative binding model to NMR data. Finally, the effect of gelator chemical design on the spatial organization of the networked gelator was probed by small-angle neutron and X-ray scattering (SANS/SAXS) on the native gel, and a tentative self-assembly model was proposed.