The objectives of open government data initiatives range from enhancing transparency and accountability to increasing innovation and participation. However, there is a lack of knowledge of the extent to which the objectives of open government data initiatives are achieved. This article investigates the relationship between the objectives of open government data initiatives and the benefits delivered. A total of 168 survey responses concerning 156 open government data initiatives at different government levels worldwide suggest that operational and technical benefits are the benefits most often delivered, followed by economic benefits and, finally, societal benefits. Surprisingly, our study suggests that whether an open government data initiative delivers a benefit (e.g. increased openness, trust or innovation) is not significantly affected by having an objective related to the delivery of that benefit. The objectives of state- and national-level open government data initiatives are more often achieved than those of local- and regional-level open government data initiatives.

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.