Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining customer willingness to pay more for banking services: the role of employee commitment, customer involvement and customer value

PurposeAlthough scholars highlighted the need to close the interactive marketing gap and enhanced understanding of willingness to pay more in settings where customer participation in the service delivery process is paramount, research addressing this issue is scare. This study investigates the effect of perceived employee commitment to service delivery and customer involvement on customer value and willingness to pay more. The study also examines the extent to which customer value mediates the effect of employee commitment and customer involvement on willingness to pay more for banking services.Design/methodology/approachThe analysis was based on a sample of 211 Nigerian bank customers procured through a mall-intercept survey technique. The partial least squares structural equation modelling procedure and the Preacher–Hayes Bootstrapping technique aided hypotheses testing.FindingsThis study demonstrates that elements of employee commitment to service delivery and customer involvement have significant positive effect on the components of customer value. It also shows that customer value components have significant effect on customers' willingness to pay more. Additionally, the study shows that components of customer value mediate the effect of employee commitment to service delivery and customer involvement on willingness to pay more.Research limitations/implicationsThe study contributes to closing gaps in interactive marketing literature by uncovering how willingness to pay more for services is influenced by customer perceptions of employee commitment (affective and calculative) service delivery, customer involvement and customer value (hedonic and utilitarian). Practical implicationsIt is important for managers to put in place measures that will help them know the kind of commitment cues their employees are emitting to customers as well as levels of customer involvement during service encounters.Originality/valueThis study breaks new ground in three unique ways. First, the study represents the first attempt to examine the combined effect of employee commitment to service delivery and customer involvement on consumer value perceptions. Second, the study also demonstrates that hedonic value has a more pronounced effect on willingness to pay more for banking services than utilitarian value. Finally, the study shows the extent to which customer value (hedonic vs utilitarian) mediates the effect of employee commitment to service delivery and customer involvement on willingness to pay more.
An integrative taxonomic framework for the study of the genus Ciona (Ascidiacea) and description of a new species, Ciona intermedia

The genus Ciona is an interesting ‘taxonomic case’ because its evolutionary history and taxonomy have not yet been resolved completely. In this study, we present new findings, describing specimens of an unidentified Ciona species collected along the north-eastern coasts of Sardinia (Tyrrhenian Sea, Mediterranean Sea). Applying an integrative taxonomic approach, based on the joint examination of morphological and molecular traits, we identify these specimens as a new species, Ciona intermedia sp. nov. Morphological comparisons and peculiarities of the habitat first revealed that these Ciona specimens have intermediate characters compared with other Ciona species. Molecular characterization (based on three mitochondrial regions: two already used for discriminating Ciona cryptic species and a newly developed one) confirmed that our specimens could not be assigned to any previously molecularly-characterized species. Both molecular phylogenetic reconstructions and morphological data clearly indicate C. intermedia as sister clade of Ciona edwardsi. Our findings add further complexity to the taxonomy of Ciona, underlying the importance of an integrative taxonomic approach for the study of the evolutionary history of this enigmatic genus.
Development of a Webinar for Sport Coaches: Suggested Best Practices for Supporting Athletes

Abstract Coaches regularly provide social support to athletes; however, few coach education programs have addressed this topic. Therefore, this project sought to develop a social support webinar for coaches. Content was based on findings from two research studies exploring support provision to athletes. The webinar was pilot tested and then delivered online to 133 coaches. An evaluation of the webinar, consisting of surveys with an open-ended feedback section, was conducted. Results indicated increased participant capability, opportunity, motivation, and behavior for providing social support. Areas for improvement, such as providing information specific to youth and para-athletes, and enhancing participant engagement, were suggested.
Transformation Models in High Dimensions

Abstract Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze transformation models in a high-dimensional setting, where the set of potential covariates is large. We propose an estimator for the transformation parameter and we show that it is asymptotically normally distributed using an orthogonalized moment condition where the nuisance functions depend on the target parameter. In a simulation study, we show that the proposed estimator works well in small samples. A common practice in labor economics is to transform wage with the log-function. In this study, we test if this transformation holds in American Community Survey (ACS) data from the United States.
To What Extent Does Illicit Drug Use Predispose to Facial Injury? An Institutional Investigation for an Emergent Problem

Study Design: There are potential substantive linkages between illicit drug use and the occurrence of injury. Objective: The purpose of our study was to determine the prevalence, class of illicit drugs abused, and demographics in relation to a cohort of patients who sustained facial injury. Methods: The authors undertook a retrospective observational study of a prospectively accessioned cohort of patients who had sustained a facial injury and presented to the John Hunter Hospital (Newcastle, NSW, Australia). The primary predictor variable was the presentation of a facial injury and the secondary outcome variables included illicit drug use, alcohol use, and socioeconomic factors. The study was carried out over a 12-month period. A descriptive analysis was undertaken on the assembled data. Results: Of the 465 patient medical records that were accessed for the study, 348 were male and 117 were female. Their average age was 42.6 years: 5.8% (n = 27) were under the influence of illicit drugs at the time of their presentation and 13.1% (n = 61) admitted to an intercurrent illicit drug habit. Those who were under the combined influence of alcohol and drugs comprised of 2.8% (n = 13). Conclusions: Our study found that illicit drug use plays a small, but nonetheless, demonstrable role in the presentation of maxillofacial trauma patients to an urban level I trauma center. Illicit drug users are an emergent sociodemographic group of patients who can sustain facial trauma and their presentation needs to be appropriately considered, assessed, and managed collectively.
Rate-Optimal Estimation of the Intercept in a Semiparametric Sample-Selection Model

This paper presents a new estimator of the intercept of a linear regression model in cases where the outcome varaible is observed subject to a selection rule. The intercept is often in this context of inherent interest; for example, in a program evaluation context, the difference between the intercepts in outcome equations for participants and non-participants can be interpreted as the difference in average outcomes of participants and their counterfactual average outcomes if they had chosen not to participate. The new estimator can under mild conditions exhibit a rate of convergence in probability equal to $n^{-p/(2p+1)}$, where $p\ge 2$ is an integer that indexes the strength of certain smoothness assumptions. This rate of convergence is shown in this context to be the optimal rate of convergence for estimation of the intercept parameter in terms of a minimax criterion. The new estimator, unlike other proposals in the literature, is under mild conditions consistent and asymptotically normal with a rate of convergence that is the same regardless of the degree to which selection depends on unobservables in the outcome equation. Simulation evidence and an empirical example are included.
Chance-Constrained AC Optimal Power Flow: A Polynomial Chaos Approach

As the share of renewables in the grid increases, the operation of power systems becomes more challenging. The present paper proposes a method to formulate and solve chance-constrained optimal power flow while explicitly considering the full nonlinear ac power flow equations and stochastic uncertainties. We use polynomial chaos expansion to model the effects of arbitrary uncertainties of finite variance, which enables to predict and optimize the system state for a range of operating conditions. We apply chance constraints to limit the probability of violations of inequality constraints. Our method incorporates a more detailed and a more flexible description of both the controllable variables and the resulting system state than previous methods. Two case studies highlight the efficacy of the method, with a focus on satisfaction of the ac power flow equations and on the accurate computation of moments of all random variables.
Double/Debiased Machine Learning for Treatment and Causal Parameters

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficients, average treatment effects, average lifts, and demand or supply elasticities. In fact, estimates of such causal parameters obtained via naively plugging ML estimators into estimating equations for such parameters can behave very poorly due to the regularization bias. Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools. Specifically, we can form an orthogonal score for the target low-dimensional parameter by combining auxiliary and main ML predictions. The score is then used to build a de-biased estimator of the target parameter which typically will converge at the fastest possible 1/root(n) rate and be approximately unbiased and normal, and from which valid confidence intervals for these parameters of interest may be constructed. The resulting method thus could be called a "double ML" method because it relies on estimating primary and auxiliary predictive models. In order to avoid overfitting, our construction also makes use of the K-fold sample splitting, which we call cross-fitting. This allows us to use a very broad set of ML predictive methods in solving the auxiliary and main prediction problems, such as random forest, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregators of these methods.
SMARThealth India: A stepped-wedge, cluster randomised controlled trial of a community health worker managed mobile health intervention for people assessed at high cardiovascular disease risk in rural India

Background Cardiovascular diseases (CVD) are rising in India resulting in major health system challenges. Methods Eighteen primary health centre (PHC) clusters in rural Andhra Pradesh were randomised over three, 6-month steps to an intervention comprising: (1) household CVD risk assessments by village-based community health workers (CHWs) using a mobile tablet device; (2) electronic referral and clinical decision support for PHC doctors; and (3) a tracking system for follow-up care. Independent data collectors screened people aged ≥ 40 years in 54 villages serviced by the PHCs to create a high CVD risk cohort (based on WHO risk charts and blood pressure thresholds). Randomly selected, independent samples, comprising 15% of this cohort, were reviewed at each 6-month step. The primary outcome was the proportion meeting systolic blood pressure (SBP) targets (<140mmHg). Findings Eight-four percent of the eligible population (n = 62,254) were assessed at baseline (18.4% at high CVD risk). Of those at high risk, 75.3% were followed up over two years. CHWs screened 85.9% of the baseline cohort and doctors followed up 70.0% of all high risk referrals. There was no difference in the proportion of people achieving SBP targets (41.2% vs 39.2%; adjusted odds ratio (OR) 1.01 95% CI 0.76–1.35) or receiving BP-lowering medications in the intervention vs control periods respectively. There was a high discordance in risk scores generated by independent data collectors and CHWs, resulting in only 37.2% of the evaluation cohort exposed to the intervention. This discordance was mainly driven by fluctuating BP values (both normal variability and marked seasonal variations). In the pre-specified high risk concordant subgroup, there was greater use of BP-lowering medications in the intervention period (54.3% vs 47.9%, OR 1.22, 95% CI 1.03–1.44) but no impact on BP control. Conclusions The strategy was well implemented with increased treatment rates among high risk individuals assessed by CHWs, however effects on BP were not demonstrated. Use of guideline-recommended BP thresholds for identifying high risk individuals substantially affected the reproducibility of risk assessment, and thus the ability to reliably evaluate the effectiveness of the intervention. In addition, unanticipated seasonal variation in BP in the context of a stepped-wedge trial highlights the inherent risks of this study design. Trial registration Clinical Trials Registry of India CTRI/2013/06/ 003753.
The VANDELS survey: the star-formation histories of massive quiescent galaxies at 1.0 < z < 1.3

We present a Bayesian full-spectral-fitting analysis of 75 massive ($M_* \gt 10^{10.3} \, \mathrm{M_\odot }$) UVJ-selected galaxies at redshifts of 1.0 < z < 1.3, combining extremely deep rest-frame ultraviolet spectroscopy from VANDELS with multiwavelength photometry. By the use of a sophisticated physical plus systematic uncertainties model, constructed within the bagpipes code, we place strong constraints on the star-formation histories (SFHs) of individual objects. We first constrain the stellar mass versus stellar age relationship, finding a steep trend towards earlier average formation time with increasing stellar mass (downsizing) of $1.48^{+0.34}_{-0.39}$ Gyr per decade in mass, although this shows signs of flattening at $M_* \gt 10^{11} \, \mathrm{M_\odot }$. We show that this is consistent with other spectroscopic studies from 0 < z < 2. This relationship places strong constraints on the AGN-feedback models used in cosmological simulations. We demonstrate that, although the relationships predicted by simba and illustristng agree well with observations at z = 0.1, they are too shallow at z = 1, predicting an evolution of ≲0.5 Gyr per decade in mass. Secondly, we consider the connections between green-valley, post-starburst, and quiescent galaxies, using our inferred SFH shapes and the distributions of galaxy physical properties on the UVJ diagram. The majority of our lowest-mass galaxies ($M_* \sim 10^{10.5} \, \mathrm{M_\odot }$) are consistent with formation in recent (z < 2), intense starburst events, with time-scales of ≲500 Myr. A second class of objects experience extended star-formation epochs before rapidly quenching, passing through both green-valley and post-starburst phases. The most massive galaxies in our sample are extreme systems: already old by z = 1, they formed at z ∼ 5 and quenched by z = 3. However, we find evidence for their continued evolution through both AGN and rejuvenated star-formation activity.
Treatment Eﬀect Models with Strategic Interaction in Treatment Decisions

This study considers treatment effect models in which others' treatment decisions can affect one's own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one's own treatment and that from the partner can be identified separately. Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. We show that the proposed estimator is uniformly consistent and asymptotically normally distributed. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance.
Structural Neural Encoders for AMR-to-text Generation

AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs. Sequence-to-sequence models can be used to this end by converting the AMR graphs to strings. Approaching the problem while working directly with graphs requires the use of graph-to-sequence models that encode the AMR graph into a vector representation. Such encoding has been shown to be beneficial in the past, and unlike sequential encoding, it allows us to explicitly capture reentrant structures in the AMR graphs. We investigate the extent to which reentrancies (nodes with multiple parents) have an impact on AMR-to-text generation by comparing graph encoders to tree encoders, where reentrancies are not preserved. We show that improvements in the treatment of reentrancies and long-range dependencies contribute to higher overall scores for graph encoders. Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of the art by 1.24 points.
Geometrically stopped Markovian random growth processes and Pareto tails

Many empirical studies document power law behavior in size distributions of economic interest such as cities, firms, income, and wealth. One mechanism for generating such behavior combines independent and identically distributed Gaussian additive shocks to log-size with a geometric age distribution. We generalize this mechanism by allowing the shocks to be non-Gaussian (but light-tailed) and dependent upon a Markov state variable. Our main results provide sharp bounds on tail probabilities, a simple equation determining Pareto exponents, and comparative statics. We present two applications: we show that (i) the tails of the wealth distribution in a heterogeneous-agent dynamic general equilibrium model with idiosyncratic investment risk are Paretian, and (ii) a random growth model for the population dynamics of Japanese municipalities is consistent with the observed Pareto exponent but only after allowing for Markovian dynamics.
Safety of Positive Pressure Extubation Technique

BACKGROUND: Laboratory studies suggest applying positive pressure without endotracheal suction during cuff deflation and extubation. Although some studies reported better physiological outcomes (e.g. arterial blood gases) with this technique, the safety of positive pressure extubation technique has not been well studied. The aim of this study was to determine the safety of the positive-pressure extubation technique compared with the traditional extubation technique in terms of incidence of complications. METHODS: Adult subjects who were critically ill and on invasive mechanical ventilation who met extubation criteria were included. The subjects were randomly assigned to positive-pressure extubation (n = 120) or to traditional extubation (n = 120). Sequential tests for noninferiority and, when appropriate, for superiority were performed. Positive pressure was considered noninferior if the upper limit of the CI for the absolute risk difference did not exceed a threshold of 15% in favor of the traditional group, both in per protocol and intention-to-treat analyses. A P value of <. 05 was considered significant. RESULTS: A total of 236 subjects were included in the primary analysis (per protocol) (119 in the positive-pressure group and 117 in the traditional group). The incidence of overall major and minor complications, pneumonia, extubation failure, and reintubation was lower in the positive-pressure group than in the traditional group, with statistical significance for noninferiority both in the per protocol (P < .001) and intention-to-treat (P < .001) analyses. The lower incidence of major complications found in the positive-pressure group reached statistical significance for the superiority comparison, both in per protocol (P = .03) and intention-to-treat (P = .049) analyses. No statistically significant differences were found in the superiority comparison for overall complications, minor complications, pneumonia, extubation failure, and reintubation. CONCLUSIONS: Positive pressure was safe and noninferior to traditional extubation methods. Furthermore, positive pressure has shown to be superior in terms of a lower incidence of major complications. (ClinicalTrials.gov registration NCT03174509.)
Who Do You Loathe? Feelings toward Politicians vs. Ordinary People in the Opposing Party

Abstract Scholars, the media, and ordinary people alike express alarm at the apparent loathing between Democrats and Republicans in the mass public. However, the evidence of such loathing typically comes from survey items that measure attitudes toward the Democratic and Republican Parties, rather than attitudes toward ordinary partisans. Using a nationally representative survey, I find that Democrats and Republicans have substantially more positive feelings toward ordinary people belonging to the opposing party than they do toward politicians in the opposing party and the opposing party itself. These results indicate that research relying on measures of feelings toward the opposing “Party” vastly overstates levels of partisan animosity in the American public and demonstrate the need to distinguish between attitudes toward party elites and ordinary partisans in future research.
Differences in Linea Alba Stiffness and Linea Alba Distortion Between Women With and Without Diastasis Recti Abdominis: The Impact of Measurement Site and Task.

STUDY DESIGN Cross-sectional, observational cohort. BACKGROUND The biomechanical implications of diastasis recti abdominis (DRA) are unknown. OBJECTIVES (1) To investigate the impact of DRA, measurement site and task on inter-rectus disstance (IRD), linea alba (LA) stiffness and LA distortion measured at rest, and during head lift and semi-curl-up tasks. (2) To describe the relationships among IRD, LA stiffness and LA distortion. METHODS B-mode ultrasound imaging and shear-wave elastography were used on a sample of 20 women. IRD, LA stiffness and LA distortion were measured at three locations while women were at rest, and repeated head lift and semi-curl-up maneuvers. All outcomes were compared between groups (DRA/no DRA), sites and tasks. Linear regression models were used to evaluate the relationships among IRD, mean and peak LA stiffness and LA distortion. RESULTS Eleven women with and nine without DRA participated. Women with DRA demonstrated lower peak and mean LA stiffness and higher LA distortion compared to women without DRA. In women with DRA, IRD and LA distortion were not influenced by measurement site; IRD decreased, LA distortion increased and LA stiffness did not change during the head lift and semi-curl-up compared to rest. In women without DRA, the LA was least stiff closest to the umbilicus; it increased in stiffness during the head lift and curl-up, and did not distort or change in IRD. CONCLUSIONS DRA is associated with low LA stiffness and distortion during a semi-curl-up task; the amount of distortion is a function of both the IRD and LA stiffness. J Orthop Sports Phys Ther, Epub 26 Mar 2019. doi:10.2519/jospt.2019. 8543.