Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

Amplitude modulated model for analyzing non-stationary speech signals

Recently amplitude modulated (AM) model in presence of additive white noise was used to analyze certain non-stationary speech data. It is observed that the assumption of white noise may not be proper in many cases. In this article, we consider the AM signal model in presence of stationary noise. We consider the least squares estimators and the estimators obtained by maximizing the Periodogram function. The two estimators are asymptotically equivalent. We study the theoretical properties of both estimators and observe their performances through numerical simulations. One speech data is analyzed and it is observed that the performance of the proposed estimators is quite satisfactory.     

Analysis of middle-censored data with exponential lifetime distributions

Recently Jammalamadaka and Mangalam [2003. Non-parametric estimation for middle censored data. J. Nonparametric Statist. 15, 253–265] introduced a general censoring scheme called the “middle-censoring” scheme in non-parametric set up. In this paper we consider this middle-censoring scheme when the lifetime distribution of the items is exponentially distributed and the censoring mechanism is independent and non-informative. In this set up, we derive the maximum likelihood estimator and study its consistency and asymptotic normality properties. We also derive the Bayes estimate of the exponential parameter under a gamma prior. Since a theoretical construction of the credible interval becomes quite difficult, we propose and implement Gibbs sampling technique to construct the credible intervals. Monte Carlo simulations are performed to evaluate the small sample behavior of the techniques proposed. A real data set is analyzed to illustrate the practical application of the proposed methods.     

A Dynamic Perspective on Diverse Teams: Moving from the Dual-Process Model to a Dynamic Coordination-based Model of Diverse Team Performance

The existing literature on diverse teams suggests that diversity is both helpful to teams in making more information available and encouraging creativity and damaging to teams in reducing cohesion and information sharing. Thus the extant literature suggests that diversity within teams is a double-edged sword that leads to both positive and negative effects simultaneously. This literature has not, however, fully embraced the increasing calls in the broader groups literature to take account of time in understanding how groups function [e.g. Cronin, M. A., Weingart, L. R., &; Todorova, G. (2011). Dynamics in groups: Are we there yet? The Academy of Management Annals, 5, 571–612]. We review the literature on diverse teams employing this lens to develop a dynamic perspective that takes account of the timing and flow of diversity's effects. Our review suggests that diversity in groups has different short-term and long-term effects in ways that are not fully captured by the currently dominant double-edged sword metaphor. We identify an emerging perspective that suggests a tropical depression metaphor—that has the potential, over time, to develop either into a dangerous hurricane or diffuse into a rainstorm that gives way to sunshine, as more apt to capture the dynamic effects of diversity in teams. We conclude by outlining an agenda for redirecting future research on diverse teams using this more dynamic perspective.     

Inventor knowledge recombination behaviors in a pharmaceutical merger: The role of intra-firm networks

We study firms' abilities to increase the generative appropriability of their knowledge by studying the knowledge recombination patterns of inventors in the context of a merger between two equally sized pharmaceutical firms. Specifically, we study inventors' choices to recombine knowledge originating in the firm with which they merge. We hypothesize that mergers focus inventors' attention to units of knowledge originating in the other firm and that therefore, inventors will choose to recombine more of this knowledge, which exists in their intra-firm network, following a merger. We also hypothesize that inventors vary in terms of their recombination choices following a merger. We explore these differences by linking inventors' network positions with their abilities and motivations to recombine knowledge originating in the other firm. Specifically, we hypothesize an inverted-U shaped relationship between centrality and knowledge recombination from the other firm and a linear relationship between brokerage and knowledge recombination from the other firm. We test our hypotheses using patent data from the merger between Bristol-Myers and Squibb and find support for our hypotheses. The paper contributes to knowledge recombination research by exploring changes in knowledge recombination dynamics following a merger and by understanding how mergers affect firms’ generative research trajectories. Practically, we suggest that managers should identify and nurture certain types of inventors following a merger to be able to better leverage the knowledge bases of merging firms.     

Developing human resource competencies: an empirical evidence

The paper conceptually defines and empirically investigates the density of work experience along with individual characteristics (cognitive ability, Conscientiousness, and Openness to Experience) and examine their influence on Human Resource (HR) competencies. 274 HR executives from diverse industry background and their supervisors participated in the data collection efforts spread over three phases. Data analyses revealed three key findings (i) executives’ density of work experience positively relates to HR competencies, (ii) executives’ cognitive ability is the strongest predictor of the positive relationship to HR competencies, and (iii) executives with high Conscientiousness tend to achieve levels of density of work experience. Relative weight analyses reinforce that cognitive ability and density of work experience are the most prominent predictors of HR competencies. Implications for practice are discussed.     

DSGE Models with Student-t Errors

This paper deals with Dynamic Stochastic General Equilibrium (DSGE) models under a multivariate student-t distribution for the structural shocks. Based on the solution algorithm of Klein (2000) and the gamma-normal representation of the t-distribution, the TaRB-MH algorithm of Chib and Ramamurthy (2010 Chib , S. , Ramamurthy , S. ( 2010 ). Tailored randomized block MCMC methods with application to DSGE models . Journal of Econometrics 108 : 19 – 38 .[Crossref], [Web of Science ®] , [Google Scholar]) is used to estimate the model. A technique for estimating the marginal likelihood of the DSGE student-t model is also provided. The methodologies are illustrated first with simulated data and then with the DSGE model of Ireland (2004 Ireland , P. N. ( 2004 ). Technology shocks in the new keynesian model . Review of Economics and Statistics 86 ( 4 ): 923 – 936 .[Crossref], [Web of Science ®] , [Google Scholar]) where the results support the t-error model in relation to the Gaussian model.