Managing talent through developmental job experiences (DJE): The mediating role of DJE between HPWS and employees' outcomes

In the competitive landscape of the 21st century, effectively managing human capital in firms is considered to be a potential source of sustainable performance. Therefore, in this study, we tested the influence of high-performance work systems, as a talent management tool, on employees’ experience of developmental jobs. Then, we tested the mediating effect of such experiences on employees’ engagement, exhaustion, performance and turnover intention. With a sample of 254 employees of a diversity of companies and sectors of activity, our findings demonstrated that high-performance practices increase engagement, via the promotion of developmental experiences of fit, which improves performance and decreases turnover intention. Besides, those practices do not control for the pressure dimension of the developmental job experiences that increases exhaustion and turnover intention despite not worsening performance.

A mediated moderation study to explain the negative relationship between job insecurity and employee supportive voice

Double degree

The impact of job insecurity on adaptive performance via burnout

Difficult and unpredictable times, due to economic instability, lead employees to feel high job insecurity. Organizations’ only way to subsistence is to search innovative ways of solving problems and find creative solutions. This study focuses on the impact that job insecurity has on adaptive performance, a recent measure integrating the response of creativity, reactivity in the face of emergencies, interpersonal adaptability, training effort, and handling work stress, and, mediated by burnout. From the responses of two questionnaires (????????1=252; ????????2=145), we conclude that job insecurity leads to exhaustion, but not to disengagement. In turn, it is the latter that demonstrates to have negative relations with some measures of adaptive performance. Thus, it is crucial to understand how organizations can minimize the inherent process.

Quadratic programming versus second order con programming in portfolio optimization

Despite the extensive literature in finding new models to replace the Markowitz model or trying to increase the accuracy of its input estimations, there is less studies about the impact on the results of using different optimization algorithms. This paper aims to add some research to this field by comparing the performance of two optimization algorithms in drawing the Markowitz Efficient Frontier and in real world investment strategies. Second order cone programming is a faster algorithm, appears to be more efficient, but is impossible to assert which algorithm is better. Quadratic Programming often shows superior performance in real investment strategies.

An effective on-the-job training model-to facilitate strategy execution

Strategy execution has been a heated topic in the management world in recent years. However, according to a survey done by the Conference Board (2014), the chief executives are so concerned about the execution in their companies and have rated it as the No.1 or No.2 most challenging issue. Many of them choose to invest in training with a purpose to harvest the most for strategy execution. Therefore, this research is trying to find out a model to design training programs that can at most contribute to the success of strategy execution with three real-life training cases done by BTS Consulting Service. It was found that strategy execution could be greatly supported by training programs that take into consideration the four factors, namely Alignment, Mindset to Change, Capability and Organization Support. Main implications of the findings are presented and discussed. Key

Implementação de um modelo de geometric semantic genetic programming para aplicação naval

Recaí sob a responsabilidade da Marinha Portuguesa a gestão da Zona Económica Exclusiva de Portugal, assegurando a sua segurança da mesma face a atividades criminosas. Para auxiliar a tarefa, é utilizado o sistema Oversee, utilizado para monitorizar a posição de todas as embarcações presentes na área afeta, permitindo a rápida intervenção da Marinha Portuguesa quando e onde necessário. No entanto, o sistema necessita de transmissões periódicas constantes originadas nas embarcações para operar corretamente – casos as transmissões sejam interrompidas, deliberada ou acidentalmente, o sistema deixa de conseguir localizar embarcações, dificultando a intervenção da Marinha. A fim de colmatar esta falha, é proposto adicionar ao sistema Oversee a capacidade de prever as posições futuras de uma embarcação com base no seu trajeto até à cessação das transmissões. Tendo em conta os grandes volumes de dados gerados pelo sistema (históricos de posições), a área de Inteligência Artificial apresenta uma possível solução para este problema. Atendendo às necessidades de resposta rápida do problema abordado, o algoritmo de Geometric Semantic Genetic Programming baseado em referências de Vanneschi et al. apresenta-se como uma possível solução, tendo já produzido bons resultados em problemas semelhantes. O presente trabalho de tese pretende integrar o algoritmo de Geometric Semantic Genetic Programming desenvolvido com o sistema Oversee, a fim de lhe conceder capacidades preditivas. Adicionalmente, será realizado um processo de análise de desempenho a fim de determinar qual a ideal parametrização do algoritmo. Pretende-se com esta tese fornecer à Marinha Portuguesa uma ferramenta capaz de auxiliar o controlo da Zona Económica Exclusiva Portuguesa, permitindo a correta intervenção da Marinha em casos onde o atual sistema não conseguiria determinar a correta posição da embarcação em questão.