5 resultados para Application method

em Repositório digital da Fundação Getúlio Vargas - FGV


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Nowadays there are many information technologies that can make a significant difference to support collaborative efforts in the workspace. The role of IT is to support group collaboration by empowering team members with the right capabilities. One way to assess capabilities is through a maturity model. This paper proposes a first version of the Collaboration-Technology Maturity model (CTMM), aiming to serve as a strategic instrument for IT managers to control and manage the adoption of Collaboration Technologies (CITs) among their organizations. Our contribution is both theoretical and practical as we propose a descriptive maturity model. Nevertheless, it is also an application method and assessment instruments. We also completed an empirical evaluation by conducting 89 assessments at Latin American companies of all sizes and industries. This extensive field exercise allowed us to not only evaluate the usefulness of the model and instruments but also investigate CIT adoption patterns in Latin America in an attempt to collect historical data to further evolve CTMM into a comparative model. Responses were used to provide conclusions on CIT adoption in Latin America with respect to three specific backgrounds: the country of origin (region), size (in number of employees) and industry type. The implications of our findings are discussed for practitioners and researchers.

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Multi-factor models constitute a useful tool to explain cross-sectional covariance in equities returns. We propose in this paper the use of irregularly spaced returns in the multi-factor model estimation and provide an empirical example with the 389 most liquid equities in the Brazilian Market. The market index shows itself significant to explain equity returns while the US$/Brazilian Real exchange rate and the Brazilian standard interest rate does not. This example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide interval forecasts.

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In assessing the economic impact of a sector or group of sectors on a single or multiregional economy, input-output analysis has proven to be a popular method. . However, there has a problem in displaying all the information that can be obtained from this analytical approach. In this paper, we have tried to set new directions in the use of input-output analysis by presenting an improved way of looking at the economic landscapes. While this is not a new concept, a new meaning is explored in this paper; essentially, it will now be possible to visualize, in a simple picture, all the relations in the economy as well as being able to view how one sector is related to the other sectors/regions in the economy. These relations can be measured in terms of structural changes, production, value added, employment, imports, etc. While all the possibilities cannot be explored in this paper, the basic idea is given here and the smart reader can uncover all the various possibilities. To illustrate the power of analysis provided by the economic landscapes, an application is made to the sugar cane complex using an interregional inputoutput system for the Brazilian economy, constructed for 2 regions (Northeast and Rest of Brazil), for the years of 1985, 1992, and 1995.

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Multi-factor models constitute a use fui tool to explain cross-sectional covariance in equities retums. We propose in this paper the use of irregularly spaced returns in the multi-factor model estimation and provide an empirical example with the 389 most liquid equities in the Brazilian Market. The market index shows itself significant to explain equity returns while the US$/Brazilian Real exchange rate and the Brazilian standard interest rate does not. This example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide intervaI forecasts.

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Researchers often rely on the t-statistic to make inference on parameters in statistical models. It is common practice to obtain critical values by simulation techniques. This paper proposes a novel numerical method to obtain an approximately similar test. This test rejects the null hypothesis when the test statistic islarger than a critical value function (CVF) of the data. We illustrate this procedure when regressors are highly persistent, a case in which commonly-used simulation methods encounter dificulties controlling size uniformly. Our approach works satisfactorily, controls size, and yields a test which outperforms the two other known similar tests.