Influential factors and performance of logistics outsourcing practices: an evidence of malaysian companies

Despite the growing trend of logistics outsourcing, there are very limited sources of literature on logistics outsourcing, especially in determining the relationship between factors influencing outsourcing and the extent of logistics outsourcing practices. In this study, we tap into the field of strategic management to help clarify the mechanisms underlying the links between factors influencing, logistics outsourcing practices and outsourcing performance. A model based on the resource based view illustrates the hypothetical connections among these variables. The data gathered from the survey were analysed using SmartPLS software. A response rate of 21 % out of the 486 firms selected was achieved and fixed as the empirical data for this study. The results of this study provide support that superior performance is correlated to the resources of the firm. The analysis shows that lack of human and physical asset capabilities, as well as transaction uncertainty influence the extent of different logistics outsourcing practices. The four logistics outsourcing practices under study were found to have a positive relationship with logistics outsourcing performance, particularly strategic focus. This study shows that although theoretically firms aim at cost reduction by employing a logistics outsourcing strategy but not proven in this study because the financial benefit was only positively contributed by one of the four logistics outsourcing practices under study. Besides that, the results from this study also support that most firms outsource their non-core activities of the logistics practices to respond to the transaction uncertainty that their business experiences.     

A new family of multivariate slash distributions

In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.     

Barriers to green innovation initiatives among manufacturers: the Malaysian case

This study investigated the internal and external barriers to green innovation initiatives among Malaysian manufacturers. Data was gathered through a survey of 153 manufacturing companies in Malaysia. Data was analyzed using the partial least squares technique. Results indicated that the barriers to green products, processes, and systems innovations are different. Issues of environmental resources, attitude and perception, business practices, government support, and customer demand were found to be the barriers to green product innovations, whereas attitude and perception, business practices, poor external partnerships, insufficient information, lack of customer demand, and environmental commercial benefits were determined to be the factors that negatively affect green process innovations. As regards green system innovation, environmental resources, attitude and perception, business practices, technical barriers, government support, and environmental and commercial benefits presented themselves as the internal and external barriers that need to be addressed. The results have important implications for managers of manufacturers that have plans of promoting green products, processes, and system innovations.     

Drivers and outcomes of eco-design initiatives: a cross-country study of Malaysia and Australia

Review of Managerial Science - In this study, we have developed and empirically tested a model of the antecedents of eco-design initiatives and their outcome using data that was collected from 255...     

Effect of green innovation on job satisfaction of electronic and electrical manufacturers’ employees through job intensity: personal innovativeness as moderator

Green innovation is currently receiving increased international attention as a result of the growing concern on natural resource degradation and environmental pollution among consumers, governments, and communities in general. Although previous studies have indicated the positive effects of green innovation on the performance of manufacturing firms, practicing green innovation may conflict with job satisfaction, which in turn could negatively impact the performance of firms. This study examines job intensity to investigate the effects of green product and process innovation on job satisfaction. In addition, the moderating effect of personal innovativeness is investigated. Data from a survey of 191 respondents in the Malaysian electronic and electrical (E&E) manufacturing firms were analysed using partial least squares technique. Results showed that green product and process innovation have a positive direct effect on job intensity and a negative indirect effect on job satisfaction through job intensity. Personal innovativeness negatively moderates the relationship between green process innovation and job intensity. Theoretical and practical contributions of this study are discussed as well.     

Bayesian analysis in multivariate regression models with conjugate priors

In this paper, we consider the full rank multivariate regression model with matrix elliptically contoured distributed errors. We formulate a conjugate prior distribution for matrix elliptical models and derive the posterior distributions of mean and scale matrices. In the sequel, some characteristics of regression matrix parameters are also proposed.     

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method–local linear neuro-fuzzy–adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.