The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and nonemergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system. This study proposes public safety data quality criteria which were developed to assess and identify the problems affecting data quality. This study is guided by design science methodology and applies three driving theories: the data information knowledge action result (DIKAR) model, the characteristics of a smart city, and a credible data quality framework. Four critical success factors were developed to ensure that high quality public safety data is collected through participatory crowdsourcing utilising voice technologies. © 2013 Bhaveer Bhana et al.