# Managing a Smart City Integrated Model through Smart Program Management
## 8 Results
### Project Interdependencies  
Project interdependencies (PI) were investigated by using commonality and variability analysis (CVA) performed by a group of seven members. Starting from Agenda Urbana [14], for each city all the information concerning each project was collected, such as description, domain, number city partner, replicability conditions, stakeholder, development period, total investment, and population. After collecting and examining all the information of each project, it was analyzed in order to extract the key terms to highlight common and variable aspects of each project. Textual analysis [55] a tool for recording customer needs, allowed us to highlight key elements according to the integrated model described in Section 5. The Smart city integrated model includes application domain, technological layer, project requirements, functionalities, technologies used, and deliverables produced (Figure 10). This textual analysis allowed to define a lean project sheet ready to be analyzed in order to evaluate the possible presence of interdependencies between the projects. Indeed, the sheets of each Smart city project were grouped together to analyze the common and variable aspects of all the projects related to a Smart city (Figure 11); for example, searching with the key term 'People' (application domain) or 'App' (deliverables produced) would elicit all the projects in that domain or that were deliverable. The result of the research for each highlighted aspect has defined a set of aspects: commonalities, which describe what all projects of the Smart city have in common and variabilities which describe distinctive and unique aspects of each smart project that contribute to the development of the Smart city (Figure 12). In addition, this phase of analysis is complemented with expert judgement to assess interdependencies. Figure 11: Technical report smart projects. Figure 10: Textual analysis. The nine Smart cities were divided among the members of the team; one city was assigned to each member except for one member who had to analyze three cities. The goal of the team was to study projects in order to identify project requirements, functionalities, technologies used, or artifacts/deliverables produced. These results were reported by each member in a technical report to discuss with the team. Overall 10 face-to-face meetings were organized: during the first nine, the projects of each city were presented in turn. During each meeting, based on the technical report produced and sent a week in advance to the team, the rest of the members evaluated the actual existence, or not, of interdependence between the projects. A correlation matrix was used to support data collection, and an example of Bologna Smart city is shown in Table 3.  
The matrix contains the list of projects both on the first column and on the first row; for each \((i,j)\) cell of the matrix, a value of 1 means that during the meetings the members of the working team identified an interdependence between project \(i\) and project \(j\). The results show that all the projects analyzed were interdependent with at least one project in the sample. Figure 13 summarizes the distribution of the total PI achieved in each correlation matrix through a box plot for all the Smart cities. In this work, for each Smart city, a box plot shows: the lowest number of PI (minimum); the largest number of PI (maximum); the middle value of PI (median, Q2); the middle  
Figure 12: Commonality and variability analysis.