Pay Transparency: Conceptualization and Implications for Employees, Employers, and Society as a Whole

Pay transparency refers to the degree to which pay communication policies and practices governing employee pay knowledge facilitate or restrict the sharing of pay-related information. While relatively few enterprises have adopted transparent pay-communication practices, a variety of institutional factors, such as government regulations and social norms, are driving employers to provide their employees with greater pay knowledge. Consensus has emerged around the existence of three main dimensions or forms of pay transparency, namely pay-outcome transparency, pay-process transparency, and pay-communication transparency. Research findings indicate that pay-outcome transparency, which relates to the degree to which pay rate information is disclosed by the employer, has both beneficial and problematic consequences, depending on the outcome. For example, while pay-outcome transparency has been consistently found to be associated with enhanced individual task performance and reduced gender-based pay discrepancies, it has also been associated with higher levels of envy, diminished helping, heightened levels of counterproductive work behavior, and pay compression (which could elicit negative sorting effects). In contrast, pay-process transparency, which relates to the degree to which employees are informed about the parameters underlying reward-related decisions, has been found to have largely beneficial consequences and few unintended negative consequences. Finally, while it is least studied, pay-communication transparency, capturing the degree to which restrictions are placed on employees’ ability to share pay knowledge with others, is positively associated with employee perceptions of employer fairness and trustworthiness and can have significant implications for employee retention.

Multi-taskLearning and Ensemble Approach to Predict Cognitive Scores for Patients with Alzheimer's Disease

The objective of this research was to explore the efficacy of integrating 1) using multi-task learning, 2) neuroimaging data and common risk factors to predict the ADAS-cog 11 score in Alzheimer's patients. We studied the magnetic resonance imaging (MRI) scans of 798 participants ranging between 0 and 96 months from the initial diagnosis. This enabled us to exploit the benefits of the U-Net architecture, a structure historically known to perform well on medical imaging tasks using limited data with heavy image augmentations. The multi-task model simultaneously performed segmentation of white matter, gray matter, and cerebrospinal fluid on the MRI input and regression to predict the ADAS-cog 11 score. There is a body of literature highlighting the independent relationships between gray and white matter volumes and dementia severity; the trajectory was to explore if the multi-task model structure for these interrelated tasks would boost performance for each individual task. The final model integrates the deep learning results with a Gradient Boosting model trained on demographic data. A considerable performance improvement from the initial multi-task U-Net on the ADAS-cog 11 prediction task was achieved across our experiments.

Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare , a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

Your Gadgets, Stress, and Performance: The Influence of Technostress on Individual Satisfaction and Performance

The Covid-19 pandemic changes the way employees work, and the use of technologies to support their work is increasing. The aim of this study is to investigate whether technologies can harm employee satisfaction and performance. The hypothesis developed stated, that the technostress creator predicted each individual role performance differently. Job satisfaction also became a mediator, whereas the technostress inhibitor was a moderator of the relationship between the technostress creator and job satisfaction. Two hundred and forty-four online responses were collected from employees in cities during the Covid-19 pandemic. Technostress (Ragu-Nathan et al., 2008), job satisfaction (Hackman & Oldham, 1976), and individual work performance (Griffin et al., 2007) questionnaires were used. The data were analyzed using path analysis. The results suggested that the technostress creator only statistically predicted individual task proficiency (ß = –0.124, SE = 0.060, and p = 0.039) and proactivity (ß = 0.134, SE = 0.060, and p = 0.026). The results found no effects from the mediator or moderator on the prediction of job satisfaction and individual role performances. Therefore, the technostress creator only increased employee stress if the technologies used disrupted their work. However, to some extent, the technostress creator can increase employee innovation when finishing work.

DEVELOPMENT OF LEAN PROCESS MANAGEMENT AT ENTERPRISES OF SOCIAL SPHERE

The article describes the logic of the development of the Lean Manufacturing concept at Russian enterprises: from a focus on the main process to projects in office activities. An approach to the implementation of projects using lean production methods in social organizations is described. The directions of activity of foreign managers in projects to improve office processes are given on the example of Daimler AG. The tasks for each of the described areas and means for their implementation are considered. The basic requirements for organizing group work and holding meetings in the “ShopFloor Management” (SFM) format are listed. An example of an integrated system of indicators in the areas of “Safety”, “Quality”, “Cost”, “Delivery”, “Motivation”, “Ecology” contains typical requirements for indicators. An approach to changing the role of a manager in relation to employees, through changing his model of behavior and organizing group work at an information stand, is described. Examples of the design of some sections of the infoboard are presented and the principles of working with them are described. An example of the design of individual task cards is given. The author compares the approach to lean management proposed by him with the methods proposed by McKinsey for the implementation of continuous improvement activities in public sector organizations.

Fast Outcome Categorization of the Upper Limb After Stroke

Background and Purpose: The ARAT (Action Research Arm Test) has been used to classify upper limb motor outcome after stroke in 1 of 3, 4, or 5 categories. The coronavirus disease 2019 (COVID-19) pandemic has encouraged the development of assessments that can be performed quickly and remotely. The aim of this study was to derive and internally validate decision trees for categorizing upper limb motor outcomes at the late subacute and chronic stages of stroke using a subset of ARAT tasks. Methods: This study retrospectively analyzed ARAT scores obtained in-person at 3 months poststroke from 333 patients. In-person ARAT scores were used to categorize patients’ 3-month upper limb outcome using classification systems with 3, 4, and 5 outcome categories. Individual task scores from in-person assessments were then used in classification and regression tree analyses to determine subsets of tasks that could accurately categorize upper limb outcome for each of the 3 classification systems. The decision trees developed using 3-month ARAT data were also applied to in-person ARAT data obtained from 157 patients at 6 months poststroke. Results: The classification and regression tree analyses produced decision trees requiring 2 to 4 ARAT tasks. The overall accuracy of the cross-validated decision trees ranged from 87.7% (SE, 1.0%) to 96.7% (SE, 2.0%). Accuracy was highest when classifying patients into one of 3 outcome categories and lowest for 5 categories. The decision trees are referred to as FOCUS (Fast Outcome Categorization of the Upper Limb After Stroke) assessments and they remained accurate for 6-month poststroke ARAT scores (overall accuracy range 83.4%–91.7%). Conclusions: A subset of ARAT tasks can accurately categorize upper limb motor outcomes after stroke. Future studies could investigate the feasibility and accuracy of categorizing outcomes using the FOCUS assessments remotely via video call.

A Critical Examination of Preservice Music Teachers’ Writing on the edTPA Portfolio

The purpose of this study was to examine the relationship between participants’ edTPA writing and edTPA portfolio scores. Specific questions included (a) Is there a relationship between overall word count and total score on the edTPA? (b) Are individual task commentary word counts associated with specific task scores and total edTPA scores? and (c) Is there a relationship between edTPA-specific vocabulary and total score on the edTPA? Written artifacts from 67 music education students who completed the K–12 Performing Arts edTPA Portfolio were collected over a 4-year period. Correlations between word counts and task and total scores were positive and of modest to moderate strength, as was the correlation between edTPA vocabulary word use and total scores. These findings are interpreted in relation to a national K–12 Performing Arts edTPA portfolio average score of 45 and existing edTPA policies affecting music teacher education programs.

Multitask adversarial attack with dispersion amplification

Recently, adversarial attacks have drawn the community’s attention as an effective tool to degrade the accuracy of neural networks. However, their actual usage in the world is limited. The main reason is that real-world machine learning systems, such as content filters or face detectors, often consist of multiple neural networks, each performing an individual task. To attack such a system, adversarial example has to pass through many distinct networks at once, which is the major challenge addressed by this paper. In this paper, we investigate multitask adversarial attacks as a threat for real-world machine learning solutions. We provide a novel black-box adversarial attack, which significantly outperforms the current state-of-the-art methods, such as Fast Gradient Sign Attack (FGSM) and Basic Iterative Method (BIM, also known as Iterative-FGSM) in the multitask setting.

Research trend analysis of Korean new graduate nurses using topic modeling

Purpose: The aim of this study is to analyze the research trends of articles on just graduated Korean nurses during the past 10 years for exploring strategies for clinical adaptation. Methods: The topics of new graduate nurses were extracted from 110 articles that have been published in Korean journals between January 2010 and July 2020. Abstracts were retrieved from 4 databases (DBpia, RISS, KISS and Google scholar). Keywords were extracted from the abstracts and cleaned using semantic morphemes. Network analysis and topic modeling were performed using the NetMiner program. Results: The core keywords included ‘education’, ‘training’, ‘program’, ‘skill’, ‘care’, ‘performance’, and ‘satisfaction’. In recent articles on new graduate nurses, three major topics were extracted by Latent Dirichlet Allocation (LDA) techniques: ‘turnover’, ‘adaptation’, ‘education’. Conclusion: Previous articles focused on exploring the factors related to the adaptation and turnover intentions of new graduate nurses. It is necessary to conduct further research focused on various interventions at the individual, task, and organizational levels to improve the retention of new graduate nurses.