Collecting and managing knowledge in smart cities

Purpose – We aim to describe how knowledge is collected in smart cities contexts and projects as knowledge management is pivotal to support strategic decision-making (Lee et al., 2013). We want to depict the set of knowledge and other resources collected (van den Hooff, de Ridder, 2004) and coordinated in smartization processes. We analyse the paths leading to the continuous improvement of the knowledge available thanks to partners involved (Lee et al., 2013) to identify how to create a potential set to improve cities’ management. We propose a model to describe knowledge collecting and mixing towards new knowledge generation (Wiig, 1997). Design/methodology/approach – We found 144 projects launched in recent years through the recognition of activities carried on and reported by industry players and Institutions when partnering local agencies in smartization; we selected the ones started at least in 2010, in order to opt for relevant information. To prevent the analysis of projects based just on episodic interventions, we focused on projects involving at least two of the smartization domains. Hence we had a final list of case studies (Yin, 2003), composed by 19 projects in different cities. These cases took into account cities from Europe, America, Asia, and Oceania. Originality/value – The most important models useful to describe the interactions emerging in smart cities contexts are based on actors. Among them there are the triple (Etzkowitz and Leydesdorff, 2008), quadruple (Lombardi et al., 2011) and quintuple (Carayannis et al., 2012) helices paying attention to the involvement of different actors as carriers of resources, without a focus on them. In our perspective the focus on resources is pivotal, leading to a more complex model useful to describe the different and several kinds of resources needed - with particular reference to knowledge - and the phases to combine them in a process. Practical implications – The model we want to propose can allow the definition of a particular set of resources, to be considered as necessary in smartization processes. Knowledge has to be considered as embedded in territories (Komninos, 2006), thus the analysis of empirical evidences lead us to reflect on customization for each city; by the way knowledge and the other resources are combined to favour the implementation of model and the circulation (Anttiroiko et al., 2013) of new knowledge can be favoured. This last outcome can allow the improvement of smart performances; hence new knowledge generation can catalyze better results