Purpose-The objective of this article is to identify the influence of the type of leadership on the performance of the project team, according to the methods applied in the management of software development projects. Design/methodology/approach-We used a quantitative method, applying a survey to project practitioners in software development teams. The sample consisted of 245 valid answers, divided into traditional and non-traditional methods. The responses were analyzed through structural equation modeling using a confirmatory methodological approach. Findings-We identified that the three styles of leadership evaluated (transactional, transformational, and empowering) are positively related to team performance, as already identified in previous studies. However, the project management method does not influence the relationship between leadership and team performance. Originality/value-The theoretical and practical contribution of this article is the finding that the type of project management used in software development (agile or traditional method) is not relevant to the choice of team leader, emphasizing that the important thing is the investment in the development of this leadership, as a measure to increase team performance, allowing flexibility in the performance of managers.

Project management is focused on planning, executing, monitoring and controlling of all aspects of a project, defined as a temporary effort to carry out a unique result, in order to achieve the targets set under the criteria of time, quality and cost restrictions. In a small or medium-sized organization focused on this type of activity, the integration of the various factors involved in the project life cycle is needed. A roadmap developed as a set of guidelines for effective project management, tailored to this type of organizations but based on the existing sets of best practices and methodological standards (traditionally oriented to huge corporations), is pursued in this work through a comprehensive-qualitative analysis added to an interview approach.

Dissolved gas analysis (DGA) is one of the most important methods to analyze fault in power transformers. In general, DGA is applied in monitoring systems based upon an autoregressive model; the current value of a time series is regressed on past values of the same series, as well as present and past values of some exogenous variables. The main difficulty is to decide the order of the autoregressive model; this means determining the number of past values to be used. This study proposes a wavelet-like transform to optimize the order of the variables in a nonlinear autoregressive neural network to predict the in oil dissolved gas concentration (DGC) from sensor data. Daubechies wavelets of different lengths are used to create representations with different time delays of ten DGC, which are then subjected to a procedure based on principal components analysis (PCA) and Pearson’s correlation to find out the order of an autoregressive model. The representations with optimal time delays for each DGC are applied as input in a multi-layer perceptron (MLP) network with backpropagation algorithm to predict the gas at the present and future times. This approach produces better results than choosing the same time delay for all inputs, as usual. The forecasts reached an average mean absolute percentage error (MAPE) of 5.763%, 1.525%, 1.831%, 2.869%, and 5.069% for C2H2, C2H6, C2H4, CH4, and H2, respectively.

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