The Service Brand as Relationships Builder

This article examines the appropriateness of the notion and application of relationship marketing in various contexts. Theoretical and conceptual similarities between the broad notions of ‘the brand’ and of relationship marketing as risk reducers, simplifiers of choice and guarantee of quality are uncovered. We put forward a notion of relationship marketing as a further step in the branding process, whereby whenever perceived risk and consumer involvement are high, relationship marketing acts as a supplementary tool enabling consumers to maintain cognitive consistency and psychological comfort. Experts' opinions regarding branding in a service context expanded on these concepts. Especially for more intangible offerings, such as financial services, the experts stressed the use of corporate brand identity as the basis of relationship building both inside and outside the organization and as a means to achieve differentiation and provide the focus for homogeneous and consistent service delivery. Finally, we suggest the concept of the service brand as a holistic process beginning with the relationship between the firm and its staff and coming alive during the interactions between staff and customers.     

A cross-cultural comparison of death anxiety: a brief note

Data are reported from samples of undergraduates around the world who have been administered Templer's Death Anxiety Scale. Data from 24 American samples and from 16 nations were identified. Strong sex differences were found and an association between the scores of men and women.     

A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Bayesian phylogenetic inference from animal mitochondrial genome arrangements

Summary. The determination of evolutionary relationships is a fundamental problem in evolutionary biology. Genome arrangement data are potentially more informative than deoxyribonucleic acid sequence data for inferring evolutionary relationships between distantly related taxa. We describe a Bayesian framework for phylogenetic inference from mitochondrial genome arrangement data using Markov chain Monte Carlo methods. We apply the method to assess evolutionary relationships between eight animal phyla.     

The use of resighting data to estimate the rate of population growth of the snail kite in Florida

The rate of population growth ( u ) is an important demographic parameter used to assess the viability of a population and to develop management and conservation agendas. We examined the use of resighting data to estimate u for the snail kite population in Florida from 1997-2000. The analyses consisted of (1) a robust design approach that derives an estimate of u from estimates of population size and (2) the Pradel (1996) temporal symmetry (TSM) approach that directly estimates u using an open-population capture-recapture model. Besides resighting data, both approaches required information on the number of unmarked individuals that were sighted during the sampling periods. The point estimates of u differed between the robust design and TSM approaches, but the 95% confidence intervals overlapped substantially. We believe the differences may be the result of sparse data and do not indicate the inappropriateness of either modelling technique. We focused on the results of the robust design because this approach provided estimates for all study years. Variation among these estimates was smaller than levels of variation among ad hoc estimates based on previously reported index statistics. We recommend that u of snail kites be estimated using capture-resighting methods rather than ad hoc counts.     

Dealing with noncompliance and missing outcomes in a randomized trial using Bayesian technology: Prevention of perinatal sepsis clinical trial,Soweto, South Africa

The success of interventions designed to address important issues in social and medical science is best addressed by randomized experiments. With human beings there are often complications, however, such as noncompliance and missing data. Such complications are often addressed by statistically invalid methods of analysis, in particular, intention-to-treat and per-protocol analyses. Here we address these two complications using a statistically valid approach based on principal stratification with a fully Bayesian analysis. This analysis is applied to a randomized trial of a potentially important intervention designed to reduce the transmission of bacterial colonization between mothers and their infants through vaginal delivery in South Africa: the Prevention of Perinatal Sepsis (PoPs).     

Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems

This article advocates the following perspective: When confronting a scientific problem, the field of statistics enters by viewing the problem as one where the scientific answer could be calculated if some missing data, hypothetical or real, were available. Thus, statistical effort should be devoted to three steps:

1. formulate the missing data that would allow this calculation,
2. stochastically fill in these missing data, and
3. do the calculations as if the filled-in data were available.

This presentation discusses: conceptual benefits, such as for causal inference using potential outcomes; computational benefits, such as afforded by using the EM algorithm and related data augmentation methods based on MCMC; and inferential benefits, such as valid interval estimation and assessment of assumptions based on multiple imputation.     

An Evaluation of Stimulus Preference Assessment Methodology in Organizational Behavior Management

The organizational behavior management literature has yielded few stimulus preference assessment methodologies for use with employees. The current investigation compared three preference assessments (ranking, survey, and multiple stimulus without replacement procedures) found in the organizational behavior management literature for their ability to predict reinforcers for direct care staff members’ behavior. In the first experiment all assessments were effective for predicting reinforcers, but the results were limited by a lack of control items in the assessment. In the second experiment the survey and ranking assessments both proved to be effective for identifying reinforcers and neutral or ineffective stimuli. Implications of these results and directions for future research are discussed.