Bayesian networks: An exploratory tool for understanding ICT adoption

Understanding technology adoption in emerging regions is challenging given the complex interrelations among socioeconomic factors that affect it directly and indirectly. The issue of impact assessment of technology adoption projects, especially the kind implemented in areas where prior technology has been very limited, is highly problematic and open to many methodological difficulties. Ethnographic evaluations have provided insight into the quality of interactions and into conceptions of technology and its adoption, whereas some quantitative analysis has been useful for high-level abstraction. In this paper, we examine the use of Bayesian networks as tools that can be used in revealing the structure of the relationships between demographic, social, and economic factors, and penetration for various technologies. Our hypothesis is that technology adoption cases in emerging regions display unique aggregated characteristics that make Bayesian network-based analysis a useful starting point in defining relationships between variables in project analysis. We compare the usability of Bayesian networks in analyzing two data sets: (1) a detailed survey focusing on 500 respondents across 14 favelas in Rio de Janeiro; and (2) a comprehensive survey of 998 users of the Akshaya tele-kiosk initiative in Kerala, India. Our illustrations show how Bayesian networks can be useful as statistical analysis tools that reveal new hypotheses, suggest unintended correlations in data, and confirm standing hypotheses.