Theoretical Framework

As the world's population is becoming progressively urban-dwelling, sustainable development challenges are increasingly concentrated in cities, placing tremendous pressure on society to build more sustainable, innovative, and equitable urban environments. Consequently, today's cities require integrated policies and new innovative ways to manage and improve the complexity of urban living conditions. The growing volume and variety of data produced in the urban ecosystem are crucial for obtaining the city's insights and building knowledge-based solutions for a smarter and more sustainable urban development. In this paper, we look at the open data impacts on these complex ecosystems and its crucial enabler role for the generation and analysis of contextual and actionable data aimed at understanding, managing, and planning the city. Despite the importance of open data, the literature is scarce in systematic and structured research that evaluates its impacts on the smart city context. This paper explores this gap by proposing a theoretical framework, composed of a model and an experiment grounded on the use of Randomized Controlled Trials (RCT), designed to give a more detailed view concerning the context and characteristics of the impacts of open data initiatives on smart cities' sustainable development. This work will contribute to open data management and smart city development, providing boundaries and theoretical insights for further research and experimentation on how open data can be leveraged to develop better smart cities.

Purpose – The image of cyclists has been increasingly recognised as an important factor in social marketing programmes aimed at increasing cycling. The purpose of this paper is to present findings from a multi-stage research project exploring image incongruencies between cyclists and non-cyclists in the UK. Design/methodology/approach – The theoretical framework of self-image congruency was used to explore a possible