# Managing a Smart City Integrated Model through Smart Program Management
## 7 Retrospective Analysis
In this way, each member of the team would be provided with all the information necessary to evaluate the existence of interdependence during the interview. * **Selection of the experimental sample**. For the selection of the experimental sample \"Convenience sampling\" was used, a specific type of non-probability sampling method based on data collected from population members [51]. The Italian scenario was selected mainly because it was feasible to analyze in comparison to the entire population (worldwide Smart cities) which would have been too large and therefore impossible to consider for both the availability of data (available only for the Italian cities) and the effort needed. Furthermore, within the Italian context, the criterion of geographical distribution of the sample was adopted to avoid potential threats to the investigation dictated by the particular economic and social context, being a factor that could influence the characteristics of the selected projects. The best rated cities for each geographical area were selected rather than chosen with a random selection, to avoid poor performances could also represent a threat for the retrospective analysis. Furthermore, given the geographical characteristics of the Italian territory we selected the same number of cities distributed between northern, central, and southern Italy. Two main sources were used to select the sample of experimental data. The first was represented by the report \"Smart City Index 2018\" [16] published at the beginning of 2019, and the second was the \"Agenda Urbana\" web portal [14]. In a study by [PERSON] and colleagues [16], 117 Italian cities were analyzed and evaluated, classifying their development in terms of networks and intelligent infrastructures and measuring their ability to innovate and offer quality services to their citizens. The evaluation associates each city with a Smart Index obtained through the combination of over 480 different indicators. It provides a general classification on a national basis (Figure 5 shows the top 39 positions in the ranking). All the smart projects carried out in each of the cities evaluated in the 2018 Smart city index report [16] are surveyed and described in detail in [14]. The selection criteria adopted was the following:  
* identify the three cities with the highest smart index in northern, central, and southern Italy;
* for each selected Smart city, include in the sample all the smart projects described in the Urban Agenda Web Portal. Initially the following cities were included in the sample: Milan, Turin, Bologna for northern Italy; Rome, Florence, and Pisa for central Italy and Bari, Cagliari, and Lecce for southern Italy. However, since there was no information for Pisa in the Urban Web Portal Agenda, it was excluded from the sample. The city classified in central Italy after Pisa was Perugia, but even in this case there was no information. We then moved on and selected the next city, Terni, which was included in the sample. As so, at the end of this process the following nine Italian Smart cities were selected: Milan, Turin, Bologna, Rome, Florence, Terni, Bari, Cagliari, and Lecce, along with 378 associated projects. The criterion of geographical distribution of the sample was adopted to avoid potential threats to the investigation dictated by the particular economic and social context, being a factor that could influence the characteristics of the selected projects. The best rated cities for each geographical area were also selected rather than using random selection, to avoid poor performances which could also represent a threat for the retrospective analysis. * **Data collection**. The information collected for each selected project were: project name; application domain; project description; project stakeholders; expected impact; conditions of replicability; project outputs. * **Data analysis**. This step was divided into three activities: 1.