Sizing Up Information Societies: Toward a Better Metric for the Cultures of ICT Adoption

Abstract

When researchers study technology diffusion in a global and comparative manner, they often find that economic productivity explains differences in the diffusion of information and communication technologies (ICTs). But when researchers study technology diffusion in a regional, national, or subnational context, they often find that politics and culture explains different diffusion rates. How do we make use of different kinds of conclusions drawn from different levels of analysis? Just knowing the ways in which wealth explains technology diffusion can obscure the ways in which politics and culture also explain patterns in technology diffusion. In this article, we offer a new perspective on weighting technology diffusion data by economic wealth to set into sharp relief the ways in which other factors—such as politics and culture—influence how well a country metabolizes new technologies. A simple but useful computation is offered, examples are assessed, and implications for public policy, industry, and research are discussed.

Keywords:

• comparative methods
• digital divide
• metrics
• technology diffusion

For their assistance and advice, the authors are grateful to Kelsey Bergman, Chase Butler, and Carrie Sturts Dossick. Particular acknowledgement is due to Charles Ragin, who demonstrated the utility of this concept for fuzzy set analysis. This research was supported by Intel's People and Practices Group, the Department of Communication at the University of Washington, and the U.S. National Science Foundation under award IIS–0713074. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The full TDI data set computed with data from 1990 to 2008 is available at www.wiareport.org and the ICPSR.

Notes

1. Further exacerbating the digital divide is the fact that there is a great difference in skill sets between populations throughout the world. This can prevent many people from taking advantage of the Internet, given that some competence in English and computer skills is necessary. This aspect of the digital divide is sometimes labeled a “second-order” digital divide.

2. Despite the salience of their findings, it might be noted that one limitation when applying the Dedrick et al. (2003) and Pohjola (2003) results to the current day is that they cover a time period before most of the digital infrastructure arrived in developing countries as well as before the dot-com crash.

3. Indeed, this makes intuitive sense as countries with good social and economic infrastructure will be more likely to invest in other improvements, such as those of a technological nature.

4. Also see the studies on China by Chase and Mulvenon (2002) and the Opennet Initiative (2005).

5. The number of personal computers may underestimate the total use of computers, especially in poor countries where computers are a shared resource, and it does not reveal the great differences in the quality of computers. It also does not take into consideration prevalence of gaming systems, mainframes, and other information technologies.

6. Estimates of Internet users are based on the subscription rates reported by in-country Internet service providers, rates that might not reflect the actual number of people using each shared Internet access point (Miller & Slater, 2000). Thus, the number of reported Internet users may be underestimated in poor countries where multiple users share computing resources belonging to friends and family, a library, or cybercafé.