Selection of logistics service provider: An analytic network process (ANP) approach

This article presents a comprehensive methodology for the selection of a logistic service provider. The proposed methodology consists of two parts: (i) preliminary screening of the available providers, and (ii) analytic network process (ANP)-based final selection. The criteria, which are relevant in the selection of a provider, have been identified and used to construct an ANP model. Thereafter, the application of ANP for the final selection of a provider has been demonstrated through an illustrative example. The results of this example indicate that compatibility between the user and the provider companies is the most important determinant, which influences the final selection process. This approach also enables the decision-makers to better understand the complex relationships of the relevant attributes in the decision-making, which may subsequently improve the reliability of the decision.     

Corporate social responsibility and multinational enterprise identity: insights from a mining company's attempt to localise in Ghana

ABSTRACT

This paper investigates how a US gold mining multinational enterprise (MNE) – one of the world's largest – operates its subsidiaries in various parts of the world by creating a unique ‘glocal identity’. The US parent company has experienced several significant challenges across its network of subsidiaries. These challenges were mostly linked to the enforcement of the MNE's identity and culture in its host environment. We contribute by describing, in detail, the attempts made by this company to localise its corporate social responsibility practices in Ghana as it sought to gain legitimacy and create an identity that would overcome the issues relating to the liability of foreignness. Our data come from a combination of sources, including questionnaires and detailed semi-structured interviews conducted with the key management employees of the mining company, members and opinion leaders of the company's host communities, and secondary sources. Our main finding is that the construction of a ‘host-friendly’ identity was centred around the mining company's involvement with the Newmont Ahafo Development Foundation.     

Bayesian Estimation for Poisson-exponential Model under Progressive Type-II Censoring Data with Binomial Removal and Its Application to Ovarian Cancer Data

In this article, we propose Maximum likelihood estimators (MLEs) and Bayes estimators of parameters of Poisson-exponential distribution (PED) under General entropy loss function (GELF) and Squared error loss function (SELF) for Progressive type-II censored data with binomial removals (PT-II CBRs). The MLEs and corresponding Bayes estimators are compared in terms of their risks based on simulated samples from PED. The proposed methodology is illustrated on a real dataset of ovarian cancer.     

Bayesian inference using product of spacings function for Progressive hybrid Type-I censoring scheme

This article is devoted to the development of product of spacings estimator for a Progressive hybrid Type-I censoring scheme with binomial removals. The experimental units are assumed to follow inverse Lindley distribution. We propose a Bayes estimator of associated scale parameter based on the product of spacings function and simultaneously compare it with that obtained under a usual Bayesian estimation procedure. The estimators are obtained under the squared error loss function along with corresponding HP intervals evaluated by using the Markov chain Monte-Carlo technique. The classical product of spacings estimator has also been derived and compared with the maximum likelihood estimator in addition to 95% average asymptotic confidence intervals. The applicability of the proposed methods is demonstrated by analysing a real data of guinea pigs affected with tuberculosis for the considered censoring scheme.     

Kernel density estimation on grouped data: the case of poverty assessment

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density estimation, in estimating poverty from grouped data. Using Monte Carlo simulations, we show that kernel density estimation gives rise to nontrivial biases in estimated poverty levels that depend on the bandwidth, kernel, poverty indicator, size of the dataset, and data generating process. Furthermore, the empirical bias in the poverty headcount ratio critically depends on the poverty line. We also undertake a sensitivity analysis of global poverty estimates to changes in the bandwidth and show that they vary widely with it. A comparison of kernel density estimation with parametric estimation of the Lorenz curve, also applied to grouped data, suggests that the latter fares better and should be the preferred approach.     

Corporate growth,sustainability and business ethics in twenty-first century

Journal of Management and Governance -     

Multidimensional Poverty in Mountainous Regions: Shan and Chin in Myanmar

Poverty is complex and multidimensional. People living in mountainous regions are vulnerable and more likely to experience multiple deprivation. However, few studies have addressed multidimensional poverty in mountainous regions. Using data from 4290 households of poverty and vulnerability assessment survey and the Alkire–Foster methodology, this paper estimate and decompose multidimensional poverty in the states of Shan and Chin in Myanmar. The multidimensional poverty is measured in five dimensions and a set of twelve indicators. Nearly half of the population in Shan and three-quarters in Chin were multidimensionally poor. The average intensity of poverty was 44% in Chin and 38% in Shan. The multidimensional poverty index was 0.33 in Chin and 0.19 in Shan. The level of multidimensional poverty in Chin was similar to that in of Sub-Saharan Africa. In Chin, 60% of the population was both multidimensionally poor and consumption poor, but in Shan, it was 20%. About 28% of the population in Shan and 15% in Chin were multidimensionally poor but not consumption poor. Deprivation in education accounts for one-third of the multidimensional poverty in Shan; while deprivation in health accounts for one-third of the multidimensional poverty in Chin. A higher proportion of multidimensionally poor had experienced shocks such as the death of a household member, agricultural loss, or death of livestock compared to the multidimensional non-poor. Multidimensional poverty was significantly higher for rural household, households with lower educational attainment, consumption poor and among those who lived in Chin. Poverty reduction programs require a holistic understanding of poverty and its different dimensions as well as the main contributing factors for effective planning and program implementation. Geographical targeting of poverty reduction program and larger investment in food, health, water, energy and education can reduce the extent of multidimensional poverty in Shan and Chin.     

Bayesian inference: Weibull Poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data

This article focuses on the parameter estimation of experimental items/units from Weibull Poisson Model under progressive type-II censoring with binomial removals (PT-II CBRs). The expectation–maximization algorithm has been used for maximum likelihood estimators (MLEs). The MLEs and Bayes estimators have been obtained under symmetric and asymmetric loss functions. Performance of competitive estimators have been studied through their simulated risks. One sample Bayes prediction and expected experiment time have also been studied. Furthermore, through real bladder cancer data set, suitability of considered model and proposed methodology have been illustrated.     

A note on the inequalities for tail probability of the multivariate normal distribution

In this paper inequalities given by Harkness Godambe (1976) for the rail probabilities of the multivariate normal distribution in the equicorrelated case are improved by using the properties of the characteristic roots of a matrix and of the convex function.