207 research outputs found

    Unit-Level Voluntary Turnover Rates and Customer Service Quality: Implications of Group Cohesiveness, Newcomer Concentration, and Size

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    Despite substantial growth in the service industry and emerging work on turnover consequences, little research examines how unit-level turnover rates affect essential customer-related outcomes. The authors propose an operational disruption framework to explain why voluntary turnover impairs customers’ service quality perceptions. Based on a sample of 75 work units and data from 5,631 employee surveys, 59,602 customer surveys, and organizational records, results indicate that unit-level voluntary turnover rates are negatively related to service quality perceptions. The authors also examine potential boundary conditions related to the disruption framework. Of three moderators studied (group cohesiveness, group size, and newcomer concentration), results show that turnover’s negative effects on service quality are more pronounced in larger units and in those with a greater concentration of newcomers

    Why High and Low Performers Leave and What They Find Elsewhere: Job Performance Effects on Employment Transitions

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    Little is known about how high and low performers differ in terms of why they leave their jobs, and no work examines whether pre-quit job performance matters for post-quit new-job outcomes. Working with a sample of approximately 2,500 former employees of an organization in the leisure and hospitality industry, we find that the reported importance of a variety of quit reasons differs both across and within performance levels. Additionally, we use an ease-of-movement perspective to predict how pre-quit performance relates to post-quit employment, new-job pay, and new-job advancement opportunity. Job type, tenure, and race interacted with performance in predicting new-job outcomes, suggesting explanations grounded in motivation, signaling, and discrimination in the external job market

    MAC Europe 1991: Evaluation of AVIRIS, GER imaging spectrometry data for the land application testsite Oberpfaffenhofen

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    During the MAC Europe 91 Campaign, the area of Oberpfaffenhofen including the land application testsite Oberpfaffenhofen was flown by the AVIRIS imaging spectrometer, the GER 2 imaging spectrometer (63 band scanner), and two SAR systems (NASA/JPL AIRSAR and DLR E-SAR). In parallel to the overflights ground spectrometry (ASD, IRIS M IV) and atmospheric measurements were carried out in order to provide data for optical sensor calibration. Ground spectrometry measurements were carried out in the runway area of the DLR research center Oberpfaffenhofen. This area was used as well during the GER 2 European flight campaign EISAC 89 as a calibration target. The land application testsite Oberpfaffenhofen is located 3 km north of the DLR research center. During the MAC Europe 91 Campaign a ground survey was carried out for documentation in the ground information data base (vegetation type, vegetation geometry, soil type, and soil mixture). Crop stands analyzed were corn, barley and rape. The DLR runway area and the land application testsite Oberpfaffenhofen were flown with the AVIRIS on 29 July and with the GER 2 on 12 and 23 July and 3 Sep. AVIRIS and GER 2 scenes were processed and atmospherically corrected for optical data analysis of optical and radar data. For the AVIRIS and the GER 2 scenes, signal-to-noise ratios (SNR) estimates were calculated. An example of the reflectance of 6 calibration targets inside a GER 2 scene of Oberpfaffenhofen is given. SNR values for the GER 2 for a medium albedo target are given. The integrated analysis for the optical and radar data was carried out in cooperation with the DLR Institute for Microwave Technologies

    Retesting in Selection: A Meta-Analysis of Practice Effects for Tests of Cognitive Ability

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    Previous studies indicate that as many as 25-50% of applicants in organizational and educational settings are retested with measures of cognitive ability. Researchers have shown that practice effects are found across measurement occasions such that scores improve when these applicants retest. This study uses meta-analysis to summarize the results of 50 studies of practice effects for tests of cognitive ability. Results from 107 samples and 134,436 participants revealed an adjusted overall effect size of .26. Moderator analyses indicated that effects were larger when practice was accompanied by test coaching, and when identical forms were used. Additional research is needed to understand the impact of retesting on the validity inferences drawn from test scores

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

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    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

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    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

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    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

    What’s past (and present) is prologue : interactions between justice levels and trajectories predicting behavioral reciprocity

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    Much of organizational justice research has tended to take a static approach, linking employees’ contemporaneous justice levels to outcomes of interest. In the present study, we tested a dynamic model emphasizing the interactive influences of both justice levels and trajectories for predicting behavioral social exchange outcomes. Specifically, our model posited both main effects and interactions between present justice levels and past justice changes over time in predicting helping behavior and voluntary turnover behavior. Data over four yearly measurement periods from 4,348 employees of a banking organization generally supported the notion that justice trajectories interact with absolute levels to predict both outcomes. Together, the findings highlight how employees invoke present fairness evaluations within the context of past fairness trends—rather than either in isolation—to inform decisions about behaviorally reciprocating at work

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

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    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
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