Mind the gap: The role of leadership in multiteam system collective cognition

The increasing prevalence of team-based organizations places a premium on leadership that will “mind the gap” and enable smooth synchronization of activities across multiple distinct teams. Prior work shows that leaders can be trained to directly facilitate between-team coordination processes. Yet, relatively little is known about the intervening psychological mechanisms that enable between-team coordination. Here, we advance multiteam-interaction mental models—cognitive structures containing knowledge of appropriate between-team activities—as one mechanism that facilitates coordination among multiple teams. We use leader and team cognition data gathered in DeChurch and Marks' (2006) MTS study to test these ideas. Results reveal leaders' multiteam-interaction mental model accuracy “transfers” to teams through strategic communication, and leader strategic communication enables between-team coordination by promoting accuracy in followers' mental models. This study highlights the importance of leadership for developing collective cognition that allows teams to “scale up” from small stand-alone teams to larger and more complex systems.     

The topology of collective leadership

Over the past decade, there has been a surge of both theoretical and empirical research to explore the possibilities of shared leadership, a reorientation of leadership away from understanding the actions and interactions of “leaders” to understanding the emergent, informal, and dynamic “leadership” brought about by the members of the collective itself. Naturally, this focus on topology (or structural patterning) has prompted researchers to leverage the advancements in network analytic methodology to understand this paradigm of leadership. Despite the recognition of the unique advantage of studying collective leadership using network analysis, there has been a translational gap. The current paper aims to fill this gap, bridging the core ideas that epitomize collective leadership to the social network metrics and analytics needed to fully understand its antecedents and consequences.     

Quality Cutting: Perceived Faculty and Staff Effects of State Budget Cuts on Institutional Quality

Product degradation is the deterioration of a commodity over time. Since the beginning of the 21st century, higher education in South Carolina has engaged in a process of cutting state funding to higher education. During this process questions emerged on the effects of the cuts on the many education programs that depend on state funding. The purpose of this study was to measure the perceptions of university faculty and administrators’ concerning the recent budget cuts in South Carolina and determine if the term product degradation can be appropriately applied to the educational product at state-supported four-year institutions in South Carolina.     

Testing for Regime Switching: A Comment

An autoregressive model with Markov regime‐switching is analyzed that reflects on the properties of the quasi‐likelihood ratio test developed by Cho and White (2007). For such a model, we show that consistency of the quasi‐maximum likelihood estimator for the population parameter values, on which consistency of the test is based, does not hold. We describe a condition that ensures consistency of the estimator and discuss the consistency of the test in the absence of consistency of the estimator.     

Missing regressor values under conditions of multicollinearity

The performance of some common procedures for replacing missing regressor values is investigated under varying conditions of multicollinearity" Analytical and numerical results indicate that the popular method of substituting sample means for missing values appears preferrable to other more sophisticated procedures given ill-conditioned designs In addition, results indicated that incomplete sample observations should not be thrown away under conditions of extreme multicollinearity.     

Large sample inference in random coefficient regression models

Random coefficient regression models have been used t odescribe repeated measures on members of a sample of n in dividuals . Previous researchers have proposed methods of estimating the mean parameters of such models. Their methods require that eachindividual be observed under the same settings of independent variablesor , lesss stringently , that the number of observations ,r , on each individual be the same. Under the latter restriction ,estimators of mean regression parameters exist which are consist ent as both r^→∞and n^→∞ and efficient as r^→∞, and large sample ( r large ) tests of mean parameters are available . These results are easily extended to the case where not a11 individuals are observed an equal number of times provided limit are taken as min(r) → ∞. Existing methods of inference , however, are not justified by the current literature when n is large and r is small, as is the case i n many bio-medical applications . The primary con tribution of the current paper is a derivation of the asymptotic properties of modifications of existing estimators as n alone tends to infinity, r fixed. From these properties it is shown that existing methods of inference, which are currently justified only when min(r) is large, are also justifiable when n is large and min(r) is small. A secondary contribution is the definition of a positive definite estimator of the covariance matrix for the random coefficients in these models. Use of this estimator avoids computational problems that can otherwise arise.     

Biostatistics Instruction at Meharry Medical College

In a variety of settings, it is desirable to display a collection of likelihoods over a common interval. One approach is simply to superimpose the likelihood curves. However, where there are more than a handful of curves, such displays are extremely difficult to decipher. An alternative is simply to display a point estimate with a confidence interval, corresponding to each likelihood. However, these may be inadequate when the likelihood is not approximately normal, as can occur with small sample sizes or nonlinear models. A second dimension is needed to gauge the relative plausibility of different parameter values. We introduce the raindrop plot, a shaded figure over the range of parameter values having log-likelihood greater than some cutoff, with height varying proportional to the difference between the log-likelihood and the cutoff. In the case of a normal likelihood, this produces a reflected parabola so that deviations from normality can be easily detected. An analogue of the raindrop plot can also be used to display estimated random effect distributions, posterior distributions, and predictive distributions.     

Reducing Boarding in a Post‐Anesthesia Care Unit

When operating room schedules in hospitals are produced, the constraints and preferences of surgeons and hospital workers are a primary consideration. The downstream impact on post‐operative bed availability is often ignored. This can lead to the boarding of patients overnight in the post‐anesthesia care unit (PACU) because intensive care unit beds are unavailable. In this paper, we apply integer programming and simulation to develop improved surgical scheduling assignments. We want to balance new surgeries with hospital discharges in order to reduce the variability of occupied beds from one day to the next and, as a result, to reduce boarding in the PACU.     

Staff experience of a new approach to family safeguarding in Oxfordshire Children's Social Care Services

This paper presents the findings from a qualitative study that sought to understand the experiences of frontline staff working in Oxfordshire County Council (OCC) Children's Social Care Services and their views on a new family safeguarding model (Family Solutions Plus). Focus group interviews were conducted with 20 frontline staff and managers in different teams across OCC Children's Social Care Services using video conferencing software. Thematic analysis identified three overarching themes: Preparation for the implementation of Family Solutions Plus, staff views on the implemented model, and challenges to its implementation. Staff voiced strong support for the new model, which places a much greater emphasis than previous practice on supporting the whole family, developing parenting skills and keeping children safe with their families. The challenges associated with the transition to a new model were considerable in the short term, partly due to the COVID-19 pandemic, but there was optimism that the new model could be sustained and stabilized over time.