Organizational Strategy and Staffing

In this chapter, we draw linkages between theory and research from strategic human resource management (and its focus on predicting unit/firm performance) with the key issues and empirical findings from the staffing and selection literature (and its focus on predicting individual performance). We organize the chapter around the fit and flexibility framework (Wright & Snell, 1998) to discuss the dual concerns of fitting staffing and selection systems to strategic needs while simultaneously enabling flexibility to respond to future demands. Implications for research and practice explain how such an approach may alter and enhance conventional views regarding staffing system characteristics such as the types of criteria, knowledge, skills, abilities, and other characteristics (KSAOs), and selection methods that are considered

Justice as a Dynamic Construct: Effects of Individual Trajectories on Distal Work Outcomes

Despite an amassing organizational justice literature, few studies have directly addressed the temporal patterning of justice judgments and the effects that changes in these perceptions have on important work outcomes. Drawing from Gestalt characteristics theory (Ariely & Cannon, 2000, 2003), we examine the concept of justice trajectories (i.e., levels and trends of individual fairness perceptions over time) and offer empirical evidence to highlight the value of considering fairness within a dynamic context. Participants included 523 working adults who completed surveys about their work experiences on 4 occasions over the course of 1 year. Results indicate that justice trends explained additional variance in distal work outcomes (job satisfaction, organizational commitment, and turnover intentions) after controlling for end-state levels of justice, demonstrating the cumulative effects of justice over time. Findings also reveal that change in procedural justice perceptions affected distal work outcomes more strongly than any other justice dimension. Implications for theory and future investigations of justice as a dynamic construct are discussed

Autonomous Intersection Driving with Neuro-Evolution

Neuro-Evolution (NE) has been used to evolve controllers in land-based vehicles that accomplish various tasks. However, there has been little work on evolving coordinated movement for maximizing traffic flow through intersections. This study used NE to synthesize collective driving behaviors for given road networks (interconnected intersections), where there were no traffic signals to assist with vehicle coordination and navigation. Rather, NE automates controller design where collective driving behavior emerges in response to the task of maximizing traffic throughput and minimizing delays at intersections

Practice effects on the modified Concept Shifting Task (mCST): A convenient assessment for treatment effects on prefrontal cognitive function

<p>Abstract</p> <p>Background</p> <p>Trail-making tests, such as the Concept Shifting Task (CST), can be used to test the effects of treatment on cognitive performance over time in various neuropsychological disorders. However, cognitive performance in such experimental designs might improve as a result of the practice obtained during repeated testing rather than the treatment itself. The current study investigated if practice affects the accuracy and duration of performance on the repeatedly administered Concept Shifting Task modified to make it resistant to practice (mCST). The mCST was administered to 54 healthy participants twice a day, before and after a short break, for eight days. Results. The ANOVA and meta-analysis showed that there was no improvement in the mCST accuracy on the last vs. the first trial (Hedges' <it>g </it>= .14, <it>p </it>= .221) or within the session (after vs. before the break on all days; <it>g </it>= .01, <it>p </it>= .922). However, the participants performed the task faster on the last vs. the first trial (<it>g </it>= -.75, <it>p </it>< .001) and after vs. before the break on all days (<it>g </it>= -.12, <it>p </it>= .002). Conclusions. Repeated administration of the mCST does not affect the accuracy of performance on the test. However, practice might contribute to faster performance on the mCST over time and within each session.</p

Work-Unit Absenteeism: Effects of Satisfaction, Commitment, Labor Market Conditions, and Time

Prior research is limited in explaining absenteeism at the unit level and over time. We developed and tested a model of unit-level absenteeism using five waves of data collected over six years from 115 work units in a large state agency. Unit-level job satisfaction, organizational commitment, and local unemployment were modeled as time-varying predictors of absenteeism. Shared satisfaction and commitment interacted in predicting absenteeism but were not related to the rate of change in absenteeism over time. Unit-level satisfaction and commitment were more strongly related to absenteeism when units were located in areas with plentiful job alternatives

Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.Comment: AIAA SciTech 201

Correlation of enhanced thrombospondin-1 expression, TGF-β signalling and proteinuria in human type-2 diabetic nephropathy

Background. Activation of the thrombospondin-1 (TSP-1)-TGF-β pathway by glucose and the relevance of TSP-1-dependent activation of TGF-β for renal matrix expansion, renal fibrosis and sclerosis have previously been demonstrated by our group in in vivo and in vitro studies

Applicant perspectives during selection

We provide a comprehensive but critical review of research on applicant reactions to selection procedures published since 2000 (n = 145), when the last major review article on applicant reactions appeared in the Journal of Management. We start by addressing the main criticisms levied against the field to determine whether applicant reactions matter to individuals and employers (“So what?”). This is followed by a consideration of “What’s new?” by conducting a comprehensive and detailed review of applicant reaction research centered upon four areas of growth: expansion of the theoretical lens, incorporation of new technology in the selection arena, internationalization of applicant reactions research, and emerging boundary conditions. Our final section focuses on “Where to next?” and offers an updated and integrated conceptual model of applicant reactions, four key challenges, and eight specific future research questions. Our conclusion is that the field demonstrates stronger research designs, with studies incorporating greater control, broader constructs, and multiple time points. There is also solid evidence that applicant reactions have significant and meaningful effects on attitudes, intentions, and behaviors. At the same time, we identify some remaining gaps in the literature and a number of critical questions that remain to be explored, particularly in light of technological and societal changes

Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Abstract. We describe our previous and current efforts towards achiev-ing an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration. Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this