Abstract

This dissertation examines the gendered nature of the scientific career for researchers in universities and national research institutes in Ghana, Kenya, and Kerala India. Employing panel data, I analyze three issues related to the diffusion of ICTs in the scientific communities of less developed areas: 1) access; 2) interaction; 3) and involvement. More specifically, I examine the way in which human capital, family structure, travel experiences, contextual factors, and technological antecedents interact with gender to influence access to and use of personal computers, email, and the Web. From there, I incorporate technological behavioral changes to predict interaction within professional networks. In the last step, I incorporate professional network measures to examine the gendered nature of research outcomes in the form of scientific productivity. The results suggest that over time ICTs have rapidly diffused within the three locations. At the same time, women continue to report less long-term access to email and the web. Furthermore, men and women are distinctly different in terms of intensity and extent of email and web use with women emerging as less technologically oriented. In spite of the differences on these measures, men are not earlier adopters of the technologies than women. It does not appear, however, that there is a consistent relationship between greater email use and integration within professional networks. Gender, on the other hand, emerges as one of the most consistent predictors of network outcomes, particularly in terms of absolute network size, geographic and gender diversity, and the proportion of male contacts reported. Finally, men and women are equally productive in domestic venues, but women are less productive in foreign venues. Furthermore, network structure is not as strongly related to productivity as are changes in technological use behavior. Respondents using email for a wider variety of reasons over time produce more in foreign and domestic venues, but intensity of email use is actually negatively related to productivity, suggesting that it is not technology use in general that matters when predicting outcomes, but the type of technology use. Network structure on the other hand, is only a significant predictor of domestic productivity.

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