Two-Stage Variables Acceptance Sampling Plans Using Process Loss Functions

In this article, a variable two-stage acceptance sampling plan is developed when the quality characteristic is evaluated through a process loss function. The plan parameters of the proposed plan are determined by using the two-point approach and tabulated according to various quality levels. Two cases are discussed when the process mean lies at the target value and when it does not, respectively. Extensive tables are provided for both cases and the results are explained with examples. The advantage of the proposed plan is compared with the existing variable single acceptance sampling plan using the process loss function.     

Essential Micro‐foundations for Contemporary Business Operations: Top Management Tangible Competencies,Relationship‐based Business Networks and Environmental Sustainability

Although various studies have emphasized linkages between firm competencies, networks and sustainability at organizational level, the links between top management tangible competencies (TMTCs) (e.g. contemporary relevant quantitative‐focused education such as big data analytics and data‐driven applications linked with the internet of things, relevant experience and analytical business applications), relationship‐based business networks (RBNs) and environmental sustainability have not been well established at micro‐level, and there is a literature gap in terms of investigating these relationships. This study examines these links based on the unique data collected from 175 top management representatives (chief executive officers and managing directors) working in food import and export firms headquartered in the UK and New Zealand. Our results from structural equation modelling indicate that TMTCs are the key determinants for building RBNs, mediating the correlation between TMTCs and environmental sustainability. Directly, the competencies also play a vital role towards environmental practices. The findings further depict that relationship‐oriented firms perform better compared to those which focus less on such networks. Consequently, our findings provide a deeper understanding of the micro‐foundations of environmental sustainability based on TMTCs rooted in the resource‐based view and RBNs entrenched in social network theory. We discuss the theoretical and practical implications of our findings, and we provide suggestions for future research.     

Reducing attitudes of prejudice and discrimination through literature: a quasi-experiment on the upper key-stage 2 students of the British schools in Karachi,Pakistan

This study aims to explore the efficacy of literature relating to Anne Frank to reduce children’s attitudes of prejudice and discrimination with respect to specific dimensions, including ethnicity and race, religion and gender. A quasi-experiment was employed with pre-post control design. The sample consisted of 100 students of the upper key-stage 2 (UKS2) level, recruited from the two British schools in Karachi. These schools represented an Experimental Group (EG) and a matched Comparison Group (CG). A research instrument titled ‘Muggles world of differences’ (MWD) was developed and validated. It was administered on CG and EG, both before and after intervention. The intervention plan ‘Reading of Young Anne’s Literature’ (ROYAL) was developed based on selected readings from Anne Frank related literature (AFL). Intervention was provided to EG, while CG studied similar concepts using a more standard method. Results of the pretest revealed a high level of prejudice and discrimination in both the EG and CG, with no significant difference (p > 0.05) in the overall scores, as well as in the specific content domains. However, the post-test of the two groups revealed that the students in the EG had outperformed their CG counterparts in overall MWD scores, as well as in specific content domains. The difference was found to be significant (p < 0.05) with the large effect size (r = ?0.63). Results support the efficacy of teaching Anne Frank related literature in reducing attitudes of prejudice and discrimination.     

IGLOO: An integrated framework for sustainable return to work in workers with common mental disorders

ABSTRACT

Current research on return to work (RTW) for employees with common mental disorders suffers from two limitations. First, research mostly focuses on the influence of resources during the absence period ignoring the resources which may facilitate sustainable RTW, i.e. employees continuing to work and thrive at work post-return. Second, research tends to view the work and non-work domains separately and fails to consider the interaction of resources at the individual, group, leader and organisational levels, once back at work. In the present position paper, we present an integrated framework and a preliminary definition of sustainable RTW. Based on current occupational health psychology theory and existing research on RTW, we develop ten propositions for the resources in and outside work, which may promote sustainable RTW. In addition to the individual, group, leader, and organisational levels, we also argue for the importance of the overarching context, i.e. the societal context and the culture and legislation that may promote sustainable RTW. Our framework raises new questions that need to be addressed to enhance our understanding of how key stakeholders can support employees with common mental health disorders staying and thriving at work.     

A three-state continuous time Markov chain model for HIV disease burden

Plasma HIV viral load (VL) is the clinical indicator used to evaluate disease burden for HIV-infected patients. We developed a covariate-adjusted, three-state, homogenous continuous time Markov chain model for HIV/AIDS disease burden among subgroups. We defined Detectable and Undetectable HIV VL levels as two transient states and Death as the third absorbing state. We implemented the exact maximum likelihood method to estimate the parameters with related asymptotic distribution to conduct hypothesis testing. We evaluated the proposed model using HIV-infected individuals from South Carolina (SC) HIV surveillance data. Using the developed model, we estimated and compared the transition hazards, transition probabilities, and the state-specific duration for HIV-infected individuals. We examined gender, race/ethnicity, age, CD4 count, place of residence, and antiretroviral treatment regimen prescribed at the beginning of the study period. We found that patients with a higher CD4 count, increased age, heterosexual orientation, white, and single tablet regimen users were associated with reduced risk of transitioning to a Detectable VL from an Undetectable VL, whereas shorter time since diagnosis, being male, and injection drug use increased the risk of the same transition.     

Big Data‐Savvy Teams’ Skills,Big Data‐Driven Actions and Business Performance

Prior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross‐functional teams’ skills and links to the role played by relevant data‐driven actions and business performance. Drawing on the resource‐based view (RBV) of the firm and on unique data collected from 240 big data experts working in global agrifood networks, we examine the links between the use of big data‐savvy (BDS) teams’ skills, big data‐driven (BDD) actions and business performance. BDS teams depend on multi‐disciplinary skills (e.g. computing, mathematics, statistics, machine learning and business domain knowledge) that help them turn their traditional business operations into modern data‐driven insights (e.g. knowing real‐time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams’ skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organizations that emphasize BDD actions perform better compared to those that do not focus on such applications and relevant insights.     

Equi-information robust designs: Which designs are possible?

The effects of missing observations in a designed experiment are reviewed. Conditions are determined for a design to retain equal information, i.e. the same generalized variance of unknown parameters, when either any single or any pair of observations is lost. Some examples of designs with this property are given. Although there are many designs which retain equal information for the loss of exactly t observations, where t = 1,2,3,…, it is shown that it is not possible to obtain any design which retains equal information when any one and any two and also any three observations are missing.     

Inference under Heteroscedasticity of Unknown Form Using an Adaptive Estimator

The heteroscedasticity consistent covariance matrix estimators are commonly used for the testing of regression coefficients when error terms of regression model are heteroscedastic. These estimators are based on the residuals obtained from the method of ordinary least squares and this method yields inefficient estimators in the presence of heteroscedasticity. It is usual practice to use estimated weighted least squares method or some adaptive methods to find efficient estimates of the regression parameters when the form of heteroscedasticity is unknown. But HCCM estimators are seldom derived from such efficient estimators for testing purposes in the available literature. The current article addresses the same concern and presents the weighted versions of HCCM estimators. Our numerical work uncovers the performance of these estimators and their finite sample properties in terms of interval estimation and null rejection rate.     

Improved CUSUM charts for monitoring process mean

In this paper, we propose new cumulative sum (CUSUM) and Shewhart-CUSUM (SCUSUM) control charts for monitoring the process mean using ranked-set sampling (RSS) and ordered RSS (ORSS) schemes. The proposed CUSUM charts include the Crosier's CUSUM (CCUSUM) and Shewhart-CCUSUM (SCCUSUM) charts using RSS, and the CUSUM, CCUSUM, SCUSUM and SCCUSUM charts using ORSS. Moreover, fast initial response features are also attached with these CUSUM charts to improve their sensitivities for an initial out-of-control situation. Monte Carlo simulations are used to compute the run length characteristics of the proposed CUSUM charts. Upon comparing the run length performances of the CUSUM charts, it turns out that the proposed CUSUM charts are more sensitive than their existing counterparts. A real dataset is used to explain the implementation of the proposed CUSUM charts.