Intervention for organizational innovation: a case study

This article is intended to report an intervention in a SME of the IT sector, aiming at an organizational change process towards a greater proactivity of employees. The presentation of the case includes the diagnosis, intervention, and the beginning of the implementation of innovation projects, based on an adapted model of third generation large-group organizational change methods. In addition to the steps followed, small-world analysis techniques were used, with the intention of determining the existing communication networks; also, a content analysis of collected success stories was made, in order to suggest strong points for a future organizational culture. The results clarified the desirable characteristics of an intervention method with large groups, adapted to Portuguese companies, and effective in organizational innovation project design. The analysis of the success stories helped to determine the strengths of an orientation for the future, while the use of measures of small-world networks allowed us to analyze the existing informal organization. Although this study does not include the completion of the projects, due to difficulties in the company, it can provide a solid basis for application in future interventions.

An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (> 15-25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982-2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series. (c) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.