Cellular Bucket Brigades on U‐Lines with Discrete Work Stations

It is challenging to maximize and maintain productivity of a U‐line with discrete stations under the impact of variability. This is because maximizing productivity requires assigning workers to suitable tasks and maintaining productivity requires sufficient flexibility in task assignment to absorb the impact of variability. To achieve this goal, we propose an operating protocol to coordinate workers on the U‐line. Under the protocol the system can be configured such that its productivity is maximized. Workers are allowed to dynamically share work so that the system can effectively absorb the impact of variability. Analysis based on a deterministic model shows that the system always converges to a fixed point or a period‐2 orbit. We identify a sufficient condition for the system to converge to the fixed point. Increasing the number of stations improves productivity only under certain circumstances. The improvement is most significant when the number of stations in each stage increases from one to two, but further dividing the U‐line into more stations has diminishing return. Simulations based on random work velocities suggest that our approach significantly outperforms an optimized, static work allocation policy if variability in velocity is large.     

Comparisons of computational methods for clustered binary data

Clustered binary data are common in medical research and can be fitted to the logistic regression model with random effects which belongs to a wider class of models called the generalized linear mixed model. The likelihood-based estimation of model parameters often has to handle intractable integration which leads to several estimation methods to overcome such difficulty. The penalized quasi-likelihood (PQL) method is the one that is very popular and computationally efficient in most cases. The expectation–maximization (EM) algorithm allows to estimate maximum-likelihood estimates, but requires to compute possibly intractable integration in the E-step. The variants of the EM algorithm to evaluate the E-step are introduced. The Monte Carlo EM (MCEM) method computes the E-step by approximating the expectation using Monte Carlo samples, while the Modified EM (MEM) method computes the E-step by approximating the expectation using the Laplace's method. All these methods involve several steps of approximation so that corresponding estimates of model parameters contain inevitable errors (large or small) induced by approximation. Understanding and quantifying discrepancy theoretically is difficult due to the complexity of approximations in each method, even though the focus is on clustered binary data. As an alternative competing computational method, we consider a non-parametric maximum-likelihood (NPML) method as well. We review and compare the PQL, MCEM, MEM and NPML methods for clustered binary data via simulation study, which will be useful for researchers when choosing an estimation method for their analysis.     

A Numerical Study of PQL Estimation Biases in Generalized Linear Mixed Models Under Heterogeneity of Random Effects

The penalized quasi-likelihood (PQL) approach is the most common estimation procedure for the generalized linear mixed model (GLMM). However, it has been noticed that the PQL tends to underestimate variance components as well as regression coefficients in the previous literature. In this article, we numerically show that the biases of variance component estimates by PQL are systematically related to the biases of regression coefficient estimates by PQL, and also show that the biases of variance component estimates by PQL increase as random effects become more heterogeneous.     

Wangye practices online: From burning effigy vessels to digital networks

This article explores the performance of a transnational network of Wangye practices, spirits, technologies, vessels, and Sinophone communities. The Wangye belief, a Fujianese popular religion performed in mainland China, Southeast Asia and Taiwan, remains widely performed. The article begins with an account of watching a YouTube recording of the live stream video of the Donggang Ying Wang religious festival in 2018. Culminating in the deity's sending off at the seaside and the burning of the NT$7 million Wangye's Boat, the video of the burning vessel remains archived online. Similarly in Malaysia, Yong Chuan Tian Temple performed the ‘Wangkang Ceremony’ in 2020, which also featured an elaborate construction of a vessel and its eventual burning. Streamed and recorded online, viewers can now witness the revelation of spirits through the conversion of material vessels into ash,smoke, and digital video. These digital enactments of religious vessels articulate a new religious re-composition that includes religious and non-religious social actors, machines, and gods, bringing old frontiers of nation and diaspora into contact.     

Avoiding Covid-19 risk information in the United States: The role of attitudes,norms, affect,social dominance orientations,and perceived trustworthiness of scientists

This study seeks guidance from the planned risk information avoidance model to explore drivers of risk information avoidance in the context of COVID-19. Data were collected early during the pandemic. Among our most notable results is that participants who are more oriented toward social dominance and are more skeptical of scientists’ credibility have (1) more supportive attitudes toward risk information avoidance and (2) feel social pressure to avoid risk information. The findings of this study highlight how the role of skepticism in science and intergroup ideologies, such as social dominance, can have important implications for how people learn about health-related information, even in times of heightened crisis.     

Extending Financial Portfolio Theory for Product Management*

This paper attempts to modify financial portfolio theory for application in product portfolio decisions. The proposed multiperiod portfolio framework should help marketers in allocating scarce corporate resources to various competing products as well as contribute to developing a body of theory to solve an important problem in marketing management. Managerial and theoretical implications are discussed.