# Managing a Smart City Integrated Model through Smart Program Management
## 10 Limitations and Threats to Validity  
In order to provide the reader with all the elements for evaluating the results presented, possible experimental threats to validity, consistency, magnitude of the results, and transferability are analyzed below.  
Threats to validity, internal validity or credibility are related to the extent that the results match the meanings and knowledge constructed in the investigated context. To increase credibility in the studies, we tried to achieve maximum variation collecting data from different projects related to different Smartcities. We selected nine Smart cities with the highest Smart index in different geographical areas: three in northern Italy, three in central Italy, and three in southern Italy. The high index of the selected cities guarantees that the results obtained are not the effect of the poor performance of the cities analyzed. The shortcomings highlighted by the analysis in terms of transversality, technological depth, and interdependencies can only increase in the case of cities with a lower index. Choosing cities in three different areas of Italy, prevents context factors such as local economy, available resources, and cultural level from being threats to validity. We then synthesized the results of our analysis across all the projects considered nationwide. This synthesis produced no contradictory evidence, increasing our confidence on the credibility of our findings.  
Consistency refers to whether the researchers did not make any inference that cannot be supported by the data. To increase consistency, we performed all data analysis in group. The analysis was performed by one researcher and reviewed by all other researchers. Member checking was also used to check the consistency of our interpretations. Inconsistencies among researchers were resolved in consensus meetings. We used the framework method to enhance the consistency of data analysis among the researchers [57].  
Finally, with respect to the magnitude of the results, in this work, 378 projects from nine Italian Smart cities were analyzed; this represents a limit as the sample size was considerably lower than the total number of Italian projects (about 1300) classified as smart. Furthermore, the selected projects are not worldwide and refer only to Italian Smart cities. Different cultural practices and issues such as the organizational and social aspects that lead the management of projects in a Smart city might have influenced the results. Therefore, we do not claim generalization of our results to a large population in a positivist perspective. Instead, consistent with our interpretive perspective, we believe that the use of multiple sources of data derived from projects across Italy and related to cities with various characteristics (e.g., dimension, culture, traditions, economy, etc.), supports good analytical generalization increasing the potential of transferability [58] of the findings to other contexts. In fact, we should keep in mind that the smart management of projects carried out in a Smart city is comparable to those of other countries. As thus the typical management of any potential project, we are confident in concluding that the results are representative. Moreover, to further strengthen the results of these findings, we are currently performing a replicated multi-country study involving projects of Smart cities in other European countries in the attempt to apply similarity-based generalization [59; 50]. Despite the impossibility of generalizing the results obtained, the authors believe that they offer interesting indications for the adoption of \"Smart program management\" in the context of a Smart city.