An assessment of manager/subordinate perceptions of performance appraisal effectiveness

This study postulates that performance appraisal will be effective to the extent that managers and subordinates have a shared perception of its purpose and function and the degree to which it meets the needs of both groups. A two part research project was conducted to: 1) identify manager/subordinate perceptions of the purposes served by the formal performance appraisal process; 2) determine the extent to which these purposes are being satisfied in organizational practice; and 3) assess if managers and subordinates have a shared perception of the effectiveness of the appraisal process. The sample for the study included 36 managers and 143 subordinates from a division of a large Midwest service organization. The results were mixed with regard to the appraisal system effectively accomplishing its diverse goals. However, in general, there was support for the notion that both managers and subordinates find the appraisal process to be a worthwhile organizational practice. Approximately 70% of the respondents indicated that they would participate in the appraisal program whether they were required to do so or not.

Reducing job-irrelevant bias in performance and appraisals: compliance and beyond

Job-irrelevant discrimination seems as ubiquitous as the performance appraisals in which it is commonly detected. This paper explores both compliance-based and more proactive approaches that deal with the various possible sources of discrimination in performance appraisal ratings. The suggestions lead to a code of practice for performance management in firms across cultures and national boundaries.

Multivariate Moments Expansion Density: Application of the Dynamic Equicorrelation Model

In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density’s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrate that it can be a useful tool for risk management purposes.

Acceleration and visualization of Dynamic Network Optimization

With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO.