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Research on the adoption of the bicycle as a means of transport has been booming in high-income countries. However, little is known about bicycle adoption in lower-income countries where air pollution is high and cycling infrastructure is poor. Understanding the drivers of cycling adoption in developing economies can increase the efficiency of transport policies while reducing local air pollution, improving health, and cutting greenhouse gas emissions. The objective of this study is to identify the factors affecting cycling uptake in a low-income country using the city of Bishkek in Kyrgyzstan as a case study. The analysis is based on the Theory of Planned Behavior, a questionnaire-based survey of 900 respondents, factor analysis, and a logit model. In contrast to studies carried out in developed countries, this study finds that students are less likely to adopt cycling than other population groups. Other findings suggest that support for public transport, a desire for regular exercise and perceptions of the environmental benefits of cycling increase the probability of the use of cycling as a mode of transport in a low-income country. The paper also identifies positive and negative perceptions of cycling among cyclists and non-cyclists. [ABSTRACT FROM AUTHOR]

This paper develops an automated algorithm to process input data for segmented string relative rankings (SSRRs). The purpose of the SSRR methodology is to create rankings of countries, companies, or any other units based on surveys of expert opinion. This is done without the use of grading systems, which can distort the results due to varying degrees of strictness among experts. However, the original SSRR approach relies on manual application, which is highly laborious and also carries a risk of human error. This paper seeks to solve this problem by further developing the SSRR approach by employing link analysis, which is based on network theory and is similar to the PageRank algorithm used by the Google search engine. The ranking data are treated as part of a linear, hierarchical network and each unit receives a score according to how many units are positioned below it in the network. This approach makes it possible to efficiently resolve contradictions among experts providing input for a ranking. A hypertext preprocessor (PHP) script for the algorithm is included in the article's appendix. The proposed methodology is suitable for use across a range of social science disciplines, especially economics, sociology, and political science. [ABSTRACT FROM AUTHOR]