Stage level measurement of information and communication technology in organizations

Na moderna Economia do Conhecimento, na Era do Big Data, entender corretamente o uso e a gestão da Tecnologia de Informação e Comunicação (TIC) tendo como base o campo acadêmico de estudos de Sistemas de Informação (SI), torna-se cada vez mais relevante e estratégico para as organizações que pretendem: permanecer em atividade, estar aptas para atender novas demandas (internas e externas) e enfrentar as complexas mudanças na competição de mercado. Esta pesquisa utiliza a teoria dos estágios de crescimento, fundamentada pelos estudos de Richard L. Nolan nos anos 70. A literatura acadêmica relacionada com modelos de estágios de crescimento e o contexto do campo de estudo de SI, fornecem as bases conceituais deste estudo. A pesquisa identifica um modelo com seus construtos relacionados aos estágios de crescimento das iniciativas da TIC/SI organizacional, partindo das variáveis de benchmark de segundo nível de Nolan, e propõe sua operacionalização com a criação e desenvolvimento de uma escala. De caráter exploratório e descritivo, a pesquisa traz contribuição teórica ao paradigma da teoria dos estágios de crescimento, adicionando um novo processo de crescimento em sua estrutura conceitual. Como resultado, é disponibilizado além de um instrumento de escala bilíngue (português e inglês), recomendações e regras para aplicação de um instrumento de pesquisa do tipo survey, na continuidade deste estudo. Como implicação geral desta pesquisa, é esperado que seu uso e aplicação ao mensurar a avaliação do nível de estágio da TIC/SI em organizações, possam auxiliar dois perfis de indivíduos: acadêmicos que estudam essa temática, assim como, profissionais que buscam respostas de suas ações práticas nas organizações onde trabalham.

Equity and Efficiency in Education: motivations and targets

The recently released "Educational PAC" attempts to place basic education at the center of the social debate. We have subsidized this debate, offering a diagnosis of how different education levels can impact individuals' lives through broad and easily interpreted indicators. Initially, we analyze how much each educational level reaches the poorest population. For example, how are those in the bottom strata of income distribution benefited by childcare centers, private secondary education, public university or adult education. The next step is to quantify the return of educational actions, such as their effects on employability and an individual's wages, and even health as perceived by the individual, be that individual poor, middle class or elite. The next part of the research presents evidence of how the main characters in education, aka mothers, fathers and children, regard education. The site available with the research presents a broad, user-friendly database, which will allow interested parties to answer their own questions relative to why people do not attend school, the time spent in the educational system and returns to education, which can all be cross-sectioned with a wide array of socio-demographic attributes (gender, income, etc.) and school characteristics (is it public, are school meals offered, etc.) to find answers to: why do young adults of a certain age not attend school? Why do they miss classes? How long is the school day? Aside from the whys and hows of teaching, the research calculates the amount of time spent in school, resulting from a combination between absence rates, evasion raters and length of the school day. The study presents ranks of indicators referring to objective and subjective aspects of education, such as the discussion of the advantages and care in establishing performance based incentives that aim at guiding the states in the race for better educational indicators.

Is 'tagging' a rationale for affirmative action in education?

In this paper, we try to rationalize the existence of one of the most common affirmative action policies: educational quotas. We model a two period economy with asymmetric information and endogenous human capital formation. Individuals may be from two different groups in the population, where each group is defined by an observable and exogenous characteristic. The distribution of skills differ across groups. We introduce educational quotas into the model by letting the planner reduce the effort cost that a student from one of the groups has to endure in order to be accepted into a university. Affirmative action policies can be interpreted as a form of ``tagging" since group characteristics are used as proxies for productivity. We find that although educational quotas are usually efficient, they need not subsidize the education of the low skill group.