Can a great resume hurt? Gender and contrast effects on recruiting

Recruiters make many inferences about applicants' abilities and interpersonal attributes on the basis of applicants' resumes. For example, every once in a while, a good resume leaves a strong positive impression and the recruiter creates a high expectation for the selection interview. What if a disappointing interview follows? Will the great resume help or hurt the candidate? The purpose of this study is to assess the impact of a good resume on the recruiter’s evaluation of a candidate when a non-enthusiastic interview follows as well as the interacting role of gender. The results of two online experiments (n=454) where participants played the role of the recruiter, showed that, on average, a very good resume (vs. no resume) before a non-enthusiastic interview did not affect the recruiter’s evaluation of the candidate. However, when the recruiter’s and the candidate’s gender were taken into consideration, a different picture emerged. While no effect was found for male recruiters, the candidate’s resume had a clear significant impact on female recruiter’s evaluations: when the candidate was also a female, the good resume shown before the non-enthusiastic interview performance tended to help, whereas when the candidate was a male, the good resume had a significant negative effect on female recruiters’ evaluation of the candidate. In sum, in situations where the resume had a strong impact on the recruiter’s evaluation (female recruiters), the direction of the effect was moderated by the candidate’s gender. Gender differences in information processing as well as in-group/out-group biases due to gender matching are used to hypothesize and explain the main findings.

Overconfidence and confirmation bias: are future managers vulnerable?

Decision makers often use ‘rules of thumb’, or heuristics, to help them handling decision situations (Kahneman and Tversky, 1979b). Those cognitive shortcuts are taken by the brain to cope with complexity and time limitation of decisions, by reducing the burden of information processing (Hodgkinson et al, 1999; Newell and Simon, 1972). Although crucial for decision-making, heuristics come at the cost of occasionally sending us off course, that is, make us fall into judgment traps (Tversky and Kahneman, 1974). Over fifty years of psychological research has shown that heuristics can lead to systematic errors, or biases, in decision-making. This study focuses on two particularly impactful biases to decision-making – the overconfidence and confirmation biases. A specific group – top management school students and recent graduates - were subject to classic experiments to measure their level of susceptibility to those biases. This population is bound to take decision positions at companies, and eventually make decisions that will impact not only their companies but society at large. The results show that this population is strongly biased by overconfidence, but less so to the confirmation bias. No significant relationship between the level of susceptibility to the overconfidence and to the confirmation bias was found.

Debt maturity determinants in Brazil: evidence from private and public corporate borrowings

This study provides an empirical investigation of the determinants of long-term debt maturity in Brazil. We built a unique database that includes privately placed debt and public debt for 308 publicly traded, non-financial Brazilian companies, from 2009 to 2013. We perform GMM panel analyses using as dependent variables the amount of long-term debt payable in more than one, three, and five years for total debt, BNDES (Brazilian Development Bank) debt and corporate bonds. The results show that the BNDES finances less risky firms, i.e., those that are larger, older, more tangible and more transparent. We also find support for information asymmetry theories, as companies with higher transparency levels have similar leverage levels relative to others but higher proportions of long-term debt in their capital structures. Regarding debt levels, we find that more levered companies are larger, less profitable, more tangible and have fewer growth opportunities. To our knowledge, this is the first paper to address the determinants of long-term debt maturity in Brazil that uses various specifications of long-term debt and that examines different types of debt.

Slow Oscillations in the Mouse Hippocampus Entrained by Nasal Respiration

Different types of network oscillations occur in different behavioral, cognitive, or vigilance states. The rodent hippocampus expresses prominentoscillations atfrequencies between 4 and 12Hz,which are superimposed by phase-coupledoscillations (30 –100Hz).These patterns entrain multineuronal activity over large distances and have been implicated in sensory information processing and memory formation. Here we report a new type of oscillation at near- frequencies (2– 4 Hz) in the hippocampus of urethane-anesthetized mice. The rhythm is highly coherent with nasal respiration and with rhythmic field potentials in the olfactory bulb: hence, we called it hippocampal respiration-induced oscillations. Despite the similarity in frequency range, several features distinguish this pattern from locally generatedoscillations: hippocampal respiration-induced oscillations have a unique laminar amplitude profile, are resistant to atropine, couple differentlytooscillations, and are abolished when nasal airflow is bypassed bytracheotomy. Hippocampal neurons are entrained by both the respiration-induced rhythm and concurrent oscillations, suggesting a direct interaction between endogenous activity in the hippocampus and nasal respiratory inputs. Our results demonstrate that nasal respiration strongly modulates hippocampal network activity in mice, providing a long-range synchronizing signal between olfactory and hippocampal networks.

Implementação e avaliação de máquinas de comitê em um ambiente com múltiplos processadores embarcados em um único chip

The number of applications based on embedded systems grows significantly every year, even with the fact that embedded systems have restrictions, and simple processing units, the performance of these has improved every day. However the complexity of applications also increase, a better performance will always be necessary. So even such advances, there are cases, which an embedded system with a single unit of processing is not sufficient to achieve the information processing in real time. To improve the performance of these systems, an implementation with parallel processing can be used in more complex applications that require high performance. The idea is to move beyond applications that already use embedded systems, exploring the use of a set of units processing working together to implement an intelligent algorithm. The number of existing works in the areas of parallel processing, systems intelligent and embedded systems is wide. However works that link these three areas to solve any problem are reduced. In this context, this work aimed to use tools available for FPGA architectures, to develop a platform with multiple processors to use in pattern classification with artificial neural networks

Modelagem de equações estruturais na análise de dados de serviços em comunicações móveis

In recent decades, changes have been occurring in the telecommunications industry, allied to competition driven by the policies of privatization and concessions, have fomented the world market irrefutably causing the emergence of a new reality. The reflections in Brazil have become evident due to the appearance of significant growth rates, getting in 2012 to provide a net operating income of 128 billion dollars, placing the country among the five major powers in the world in mobile communications. In this context, an issue of increasing importance to the financial health of companies is their ability to retain their customers, as well as turn them into loyal customers. The appearance of infidelity from customer operators has been generating monthly rates shutdowns about two to four percent per month accounting for business management one of its biggest challenges, since capturing a new customer has meant an expenditure greater than five times to retention. For this purpose, models have been developed by means of structural equation modeling to identify the relationships between the various determinants of customer loyalty in the context of services. The original contribution of this thesis is to develop a model for loyalty from the identification of relationships between determinants of satisfaction (latent variables) and the inclusion of attributes that determine the perceptions of service quality for the mobile communications industry, such as quality, satisfaction, value, trust, expectation and loyalty. It is a qualitative research which will be conducted with customers of operators through simple random sampling technique, using structured questionnaires. As a result, the proposed model and statistical evaluations should enable operators to conclude that customer loyalty is directly influenced by technical and operational quality of the services offered, as well as provide a satisfaction index for the mobile communication segment

Escalabilidade Paralela de um Algoritmo de Migração Reversa no Tempo (RTM) Pré-empilhamento

The seismic method is of extreme importance in geophysics. Mainly associated with oil exploration, this line of research focuses most of all investment in this area. The acquisition, processing and interpretation of seismic data are the parts that instantiate a seismic study. Seismic processing in particular is focused on the imaging that represents the geological structures in subsurface. Seismic processing has evolved significantly in recent decades due to the demands of the oil industry, and also due to the technological advances of hardware that achieved higher storage and digital information processing capabilities, which enabled the development of more sophisticated processing algorithms such as the ones that use of parallel architectures. One of the most important steps in seismic processing is imaging. Migration of seismic data is one of the techniques used for imaging, with the goal of obtaining a seismic section image that represents the geological structures the most accurately and faithfully as possible. The result of migration is a 2D or 3D image which it is possible to identify faults and salt domes among other structures of interest, such as potential hydrocarbon reservoirs. However, a migration fulfilled with quality and accuracy may be a long time consuming process, due to the mathematical algorithm heuristics and the extensive amount of data inputs and outputs involved in this process, which may take days, weeks and even months of uninterrupted execution on the supercomputers, representing large computational and financial costs, that could derail the implementation of these methods. Aiming at performance improvement, this work conducted the core parallelization of a Reverse Time Migration (RTM) algorithm, using the parallel programming model Open Multi-Processing (OpenMP), due to the large computational effort required by this migration technique. Furthermore, analyzes such as speedup, efficiency were performed, and ultimately, the identification of the algorithmic scalability degree with respect to the technological advancement expected by future processors