Impact of customer loyalty and service operations on customer behaviour and firm performance: empirical evidence from UK retail sector

Abstract

Retail networks are striving to achieve competitive advantage by increasing value through loyalty and efficiency with a focus on service operations. As sales promotions have become an integral part of the retail supply chain planning, customer behavioural aspects based on loyalty and service operations have been challenged greatly. Subsequently, management capabilities, such as planning and timely replenishment, have become complicated tasks for many retail store managers. This study develops a model integrating retail network value and efficiencies with customer behaviour and performance. We validate the model using survey data from prominent U.K. retail store customers. Our data analysis shows that both loyalty and service operation attributes have positive significant impact on customer behaviour, while the service operation mediates the relationship between loyalty and customer behaviour. This result gives a new outlook to build managerial capability based on customer loyalty and service operations. Our results specifically show that the service operation attributes will indirectly influence the customers’ buying behaviour even in the presence of loyalty attribute such as promotion schemes. This result sends a strong signal to retail supply chain managers to offer customised promotions considering local community rather than having uniform sales promotion nationwide.     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

Second‐Generation Immigrants? The “2.5 Generation” in the United States*

Objective. This article takes issue with the way that second‐generation immigrants have been traditionally defined. In most studies, respondents are considered to be “second generation” if they are born in the United States and if at least one of their parents was born outside the United States. This article considers whether the experiences and outcomes of those with one U.S.‐born parent and one foreign‐born parent (the “2.5 generation”) are different from those with no U.S.‐born parents (the “2.0 generation”) and those with two native‐born parents (the “third generation”). Methods. The article analyzes data from the March Current Population Survey (CPS) from 1999 to 2001. Results. The evidence indicates that the 2.5 generation is a numerically significant population, and that it varies from other groups in age structure, racial identification, educational attainment, and income. Conclusions. In studying the U.S.‐born children of immigrants, scholars should avoid lumping together the 2.5 generation with those who have no native‐born parents. The members of the 2.5 generation also should be treated as distinct from those born in the United States to two native‐born parents.     

Civic Spaces: Mexican Hometown Associations and Immigrant Participation

The study of civic participation and social capital in the United States has, until recently, been silent on the role of immigrant-serving organizations. There is a new line of scholarship, which indicates that ethnic organizations are generally disadvantaged in relation to White mainstream organizations on factors such as resources and political visibility. Our fieldwork on Mexican hometown associations (HTAs) in Los Angeles shows that transnational associations are even more disadvantaged than ethnic organizations that primarily serve the native born. However, this marginality leaves some counterintuitive advantages, namely the creation of safe spaces where undocumented immigrants, recent immigrants, and those with limited English proficiency can get involved in civic and political activities. We explore the extent to which these dynamics vary by gender and immigrant generation, and over time as Mexican hometown associations increasingly turn their attention to political issues in the United States.     

ORDER SELECTION IN ARMA MODELS USING THE FOCUSED INFORMATION CRITERION

This paper develops a new approach for order selection in autoregressive moving average models using the focused information criterion. This criterion minimizes the asymptotic mean squared error of the estimator of a parameter of interest. Simulation studies indicate that the suggested criterion is quite effective and comparable to the Akaike information criterion, the corrected Akaike information criterion and the Bayesian information criterion in autoregressive moving average order selection. The use of the focused information criterion for the simultaneous selection of regression variables and order of the error process in a linear regression model with autoregressive moving average errors is also considered.     

Application of Multiple Imputation in Analysis of Data from Clinical Trials with Treatment Related Dropouts

This article develops a functional form of the generalized Poisson regression model that parametrically nests the Poisson and the two well known generalized Poisson regression models (GP-1 and GP-2). The proposed model is applied on the Malaysian motor insurance claim count data.     

A Numerical Method for Estimating the Variance of Age at Maximum Growth Rate in Growth Models

Studies on maturation and body composition mention age at peak height velocity (PHV) as an important measure that could predict adulthood outcome. The age at PHV is often derived from growth models such as the triple logistic fitted to the stature (height) data. Theoretically, for a well-behaved growth function, age at PHV could be obtained by setting the second derivative of the growth function to zero and solving for age. Such a solution obviously depends on the parameters of the growth function. Therefore, the uncertainty in the estimation of age at PHV resulting from the uncertainty in the estimation of the growth model, need to be accounted for in the models in which it is used as a predictor. Explicit expressions for the age at PHV and, consequently the variance of the estimate of the age at PHV, do not exist for some of the commonly used nonlinear growth functions, such as the triple logistic function. Once an estimate of this variance is obtained, it could be incorporated in subsequent modeling either through measurement error models or by using the inverse variances as weights. A numerical method for estimating the variance is implemented. The accuracy of this method is demonstrated through comparisons in models where explicit solution for the variance exists. The method of estimating the variance is illustrated by applying to growth data from the Fels study and subsequently used as weights in modeling two adulthood outcomes from the same study.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

Probabilistic frontier regression models for binary type output data

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.     

RETURN ON INVESTMENT AS A CRITERION FOR INVENTORY MODELS

In the past most inventory formulations have utilized cost minimization or profit maximization as an optimizing criterion. When viewed from the standpoint of the owner or investor, maximizing the return on investment (ROI) is an appropriate criterion for many types of inventories. This paper proposes ROI as a criterion for inventory models and derives optimal reorder rules for some common assumptions. An economic order quantity that differs greatly from the traditional formulas is discussed. The paper also enumerates the conditions under which ROI is an appropriate criterion and contrasts it to the traditional cost minimization and profit maximization criteria.