The Interdisciplinary Future of Supply Chain Management Research

Since the term supply chain management (SCM) was first introduced over 30 years ago, the field of SCM has undergone numerous transformations. Today, it is a prevailing theme in scholarly and popular research, and numerous disciplines claim its ownership. However, current and emerging SCM research challenges are becoming increasingly more complex. Fragmented and "siloed" research efforts across a range of disciplines may not be adequate to address the full complexities associated with these phenomena. We interviewed 50 SCM academic thought leaders, across a range of disciplines, and 20 SCM executives with the goal of helping researchers develop a consistent stream of work that builds on existing research, identifies relevant research directions, and provides guidance for the future. Although there were some differences in opinions, there was consensus on the boundary characteristics of SCM, as well as the identification of interdisciplinary research (IDR) as necessary for the enablement of SCM to address the numerous current and emerging intractable SCM challenges. In this article, we present these findings and provide a path forward based on the collective wisdom of these academics and executives.     

A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging

Studies on diffusion tensor imaging (DTI) quantify the diffusion of water molecules in a brain voxel using an estimated 3 × 3 symmetric positive definite (p.d.) diffusion tensor matrix. Due to the challenges associated with modelling matrix‐variate responses, the voxel‐level DTI data are usually summarized by univariate quantities, such as fractional anisotropy. This approach leads to evident loss of information. Furthermore, DTI analyses often ignore the spatial association among neighbouring voxels, leading to imprecise estimates. Although the spatial modelling literature is rich, modelling spatially dependent p.d. matrices is challenging. To mitigate these issues, we propose a matrix‐variate Bayesian semiparametric mixture model, where the p.d. matrices are distributed as a mixture of inverse Wishart distributions, with the spatial dependence captured by a Markov model for the mixture component labels. Related Bayesian computing is facilitated by conjugacy results and use of the double Metropolis–Hastings algorithm. Our simulation study shows that the proposed method is more powerful than competing non‐spatial methods. We also apply our method to investigate the effect of cocaine use on brain microstructure. By extending spatial statistics to matrix‐variate data, we contribute to providing a novel and computationally tractable inferential tool for DTI analysis.     

A grouped beta process model for multivariate resting‐state EEG microstate analysis on twins

EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstate is stable, but activity switches rapidly between different microstates in a nonrandom way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behaviour. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the nonrandom state switching dynamics of the EEG activity and a VAR model defines the behaviour of all time points within a given state. We analyze the resting‐state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant, where each epoch corresponds to 2 s of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin‐pair similarity, using an Indian buffet process, to consider an infinite library of microstates, allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states that describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model, enforcing within‐participant similarity.     

Hospital Community Benefit in Rural Appalachia: One More Gap*

Non‐profit hospitals are important anchors in Appalachian communities, in part because of the concentration of health care dollars within these institutions. Community benefit efforts of these hospitals, therefore, have the potential to fill gaps in public health and social service provision in underserved areas with documented health disparities and access barriers. To date, however, we do not fully understand how community benefit practices vary by hospital setting. Employing hierarchical linear modeling using a multilevel mixed‐effects approach, this study analyzes data from the years 2010 to 2016 to assess community benefit practices and spending between hospitals in Appalachia and non‐Appalachian counties. Findings indicate that hospitals within Appalachian counties, and rural hospitals in this region, in particular, spent less on community benefit than hospitals, not in this region. Given the potential for community benefit to impact health outcomes and access to care, this disparity is important to state and local public health efforts and suggests the need for additional support for hospitals to engage their communities around critical health needs.     

Education and Social Desirability Bias: The Case of a Black Presidential Candidate*

Objective. Survey research consistently reports a positive association between educational attainment and socially tolerant attitudes, but critics hold that respondents with high levels of education may simply purport to hold attitudes seen as socially desirable. In this article, we seek to adjudicate between the claim that the association between education and social tolerance is simply an artifact of sophisticated social desirability reporting on the part of well‐educated respondents and the competing theory that education has a real impact on increasing forms of social tolerance. Methods. Using support for a black presidential candidate as our measure of social tolerance, we utilize an innovative online list experiment to test whether high levels of support are inflated because of social desirability reporting among the educational elite. Results. We find no evidence of systematic overreporting of support for a black presidential candidate among respondents with high levels of education, and note that social desirability bias declines as educational attainment increases. Conclusions. This research bolsters arguments about the liberalizing effect of education on socially tolerant attitudes, and challenges evidence that attributes this relationship to high levels of social desirability bias.     

Subterranean termites in the urban landscape: Understanding their social structure is the key to successfully implementing population management using bait technology

Subterranean termites will likely continue to be major economic insect pests in the urban environment. Public concerns about pesticide use will not abate but continue to drive development and implementation of environmentally more acceptable control strategies for subterranean termites. Termite baits are a promising alternative to soil termiticide treatments for protection of structures, but the correlation between killing termites in the landscape and protecting structures is tenuous given our current understanding of termite biology. We discuss the problems associated with defining the efficacy of termite bait products in regard to determining termite population parameters in the field. This discussion is in the form of six case histories involving measures of termite activity and relatedness taken over a three-year period. Our findings highlight the need to include control populations in studies of termite bait efficacy, the mobility of termite populations over time, the importance of defining the techniques used to describe termite colony associations, and the need to use a multidisciplinary research approach in addressing the question of termite social structure.     

Random-Effects Modeling of Categorical Response Data

In many applications observations have some type of clustering, with observations within clusters tending to be correlated. A common instance of this occurs when each subject in the sample undergoes repeated measurement, in which case a cluster consists of the set of observations for the subject. One approach to modeling clustered data introduces cluster-level random effects into the model. The use of random effects in linear models for normal responses is well established. By contrast, random effects have only recently seen much use in models for categorical data. This chapter surveys a variety of potential social science applications of random effects modeling of categorical data. Applications discussed include repeated measurement for binary or ordinal responses, shrinkage to improve multiparameter estimation of a set of proportions or rates, multivariate latent variable modeling, hierarchically structured modeling, and cluster sampling. The models discussed belong to the class of generalized linear mixed models (GLMMs), an extension of ordinary linear models that permits nonnormal response variables and both fixed and random effects in the predictor term. The models are GLMMs for either binomial or Poisson response variables, although we also present extensions to multicategory (nominal or ordinal) responses. We also summarize some of the technical issues of model-fitting that complicate the fitting of GLMMs even with existing software.     

Health informatics for older people: a review of ICT facilitated integrated care for older people

Growing demands on welfare services, arising from expanding populations of older people in many countries, has led policy makers to consider the use of information and communications technologies (ICTs) as a means to transform the cost-effective delivery of health and social care. The evidence for these claims is examined by reporting the main findings of a review of worldwide published literature documenting the adoption of health informatics applications to improve health and social care for older people. It focuses around two dimensions of the UK government's programme for 'modernising' public services, which emphasise the use of ICTs to facilitate the sharing of health and social services information and its potential to foster person-centred approaches to independent living. Findings suggest that there is little evidence that these dimensions have been realised in practice and the perceived incompatibility between them is more likely to produce expensive and ineffective health informatics outcomes.