Background: In a highly competitive world, leaders of firms highly dependent on innovation, such astelecom companies, must acquire data-driven managerial skills to systematically analyze datasets from multiple points of view to aid decision-making in the new context of Industry 4.0. Data mining can be performed on both tangible and intangible assets of Big Data sets, but systematic analytics performed on Small data can function as a crucial refinement for such insights. In addition, they are usable to train the algorithms during machine learning supervised stage, for example, when treating datasets in the field of psychometrics: originated by human perceptions and behaviors. This applies to the exploitation of strategic information, for business purposes, from intangible reservoirs, such as human capital aspects. Ambidexterity is a leadership conduct, primarily focusing on human capital and encompassing the behaviors of exploration and exploitation of new ideas. It has been historically proven to be essential for innovation. However, leaders and companies often limitedly focus on the exploitation of human capital aspects through psychometrics inserted in a data-driven framework. For business models that consider innovation as a matter to be pursued at any levels of the organization and not only confined to one specific department such as R&D, this is indeed a crucial element to be investigated to foster innovation and retaining a competitive edge. This research is performed in collaboration with a world leading telecom company and has been requested by its Innovation Leader. Objectives: The first objective of the research is to provide a flexible conceptual model and standardized methodology, suitable for incumbent, cross-country companies, highly dependent on innovation that intend to begin investigation on how those aspects influence their business performances. Second, the hypothesis testing of the conceptual model has the purpose of identifying the human capital aspects of national and corporate culture that show statistically significant andstronger cause-effect relationship towards enhancing innovation ambidexterity. Third, predictions interms of prevalence of explorative or exploitative innovative behaviors are aimed at providing indications on what the company could expect in terms of Innovation Ambidexterity with their current conditions. An automatable and replicable method that is data-driven-based for company`s decision makers is provided. It is also suitable for further integration within machine learning algorithms or simply as refinement of data mining insights and these aspects addressed are within the possibilities for improvement. The objective of the thesis is to test the methodology on a relatively small size sample to show to the company executives and Innovation Leader, the potential of the approach and the value that these data can have for decision making. They can decide to develop further the research involving larger samples at a later stage: inserting the analyses into an automatic periodical routine with dashboarding of the outcomes. During the post survey interviews, awareness among the management and executives has also been raised about the potential of such approach to obtain strategic business information unavailable until now. Please note that it was not the purpose of thisstudy to provide a conceptual model that was specific and suitable for the human capital`s characteristics of one specific company. The purpose was instead to provide a data-driven framework and a conceptual model that could be used by any company of the telecom sector to approach the task and to find moderating or mediating factors. It will also allow companies of different sectors to refine the model based on their needs at a later stage, as a possibility for future improvement. Methodology: A conceptual model, partially newly designed for this research is introduced. It incorporates selected elements of national and corporate culture appearing to be crucial for innovation ambidexterity, according to an extensive literature review. The quantitative analysis is also extensive.A less extensive analysis would have left too much uncertainty in the findings, undermining the confidence of executives in taking into consideration the results aimed at business actions. For these reasons, we recommend to researchers who are tackling the exploitation of intangible assets (such as human capital) to perform an extensive set of analyses. From the main dataset, the analyses of the methodology have been replicated on 5 sub-data sets based on the heterogeneity measured. The methodology includes CTA, PLS-SEM modeling on the outer model, PLS-SEM on the inner model including bootstrapping, MGA, FIMIX-PLS, IPMA, blindfolding for the predictive relevance of the model followed by POS and Weka predictions. Cause-effect relationships, mediating and moderating factors of national and internal culture have been also identified and indicated as part of the possiblefuture personalization of the model on the specific company`s human capital characteristics. The national culture attributes consist of power distance, uncertainty avoidance, collectivism, masculinity (unrelated to the gender) and gender diversity. The corporate culture attributes are categorized into caring climate, creative instability, boundary spanning, decision making and strategic horizon. The methodology employs a bottom-up survey design to collect data through an online questionnaire across three company sites located in Sweden, Italy, and China. The pieces of software used were SmartPLS 3 for Structural Equation Modeling and Predictions Oriented Segmentation and Weka 3.8.5 for a machine learning algorithm (an artificial neural network was used), as a double check on PLS-POS predictions. Some qualitative interpretations, pre and post survey interviews were also added. Results: Hypothesis testing and cross-comparisons are performed on groups such as employees, leaders, and the different geographical sites. During the evaluation of the results, special attention was put on the parameters related to the quality and statistical relevance, not only of the model tested on the six cohorts, but also on the single national and corporate attributes that build it up. The results show that explorative behaviors predict innovation ambidexterity to a larger extent than purely exploitative ones, confirming the main hypothesis. Predictions that were POS-based and verified by Weka machine learning algorithm have shown instead how the pursuit of innovation ambidexterity within the company is unbalanced towards exploitative behaviors. The study has provided PLS-SEM indications on how company executives may wish to pursue explorative behaviors towards innovation, but the company middle management is steering in the opposite direction, focusing on attributes more linked to efficiency and constant delivery. Consequently, what initially appeared to be a complex national culture issue of employees interfering with corporate culture, has been linked instead to a possible middle management issue related to two different business models: where one prevails over the other, instead of cooperating to reach innovation ambidexterity. This is a valuable strategic input for the company executives. The quantitative methodology uncovered results and patterns that the Innovation Leader had so far only intuitively perceived, and it offered such counterintuitive interpretation of the causes. With regards to national culture: power distance increases exploitative behaviors; gender diversity increases explorative behaviors, while it decreases exploitative behaviors. With regards to corporate culture: creative instability crucially increases explorative behaviors but decreases exploitative behaviors. Boundary spanning decreases exploitative behaviors. Conclusions: The thesis answered to the research question. It provided a scientific contribution, allowing a better understanding of how national and corporate cultures interact to generate explorativeand exploitative behaviors and ultimately innovation ambidexterity. It provided a flexible conceptual model and a standardized, automatable data-driven methodology suitable to discover insights from human capital aspects that influence innovation in a business: taking the analyses of human capital data performed by the firm “to the next level”. Recommendations for future research: A recommendation is to apply the proposed conceptual model to compare bigger size samples with even less heterogeneity, according to the optimal datasample`s characteristics identified. This will also allow a further personalization of the flexible andgeneral conceptual model presented (which is so far suitable for the general telecommunication sector), to more specific characteristics of the company which is the object of analysis. In a completely automated framework, it is also recommended to consider the possibilities of applying thisdata-driven, decision-making approach to other companies or industrial domains. This means, for example, integrating the proposed methodology within a machine learning algorithm in its supervised stage. The algorithm can be trained using the current analyses as refinement of insights provided by Big Data mining performed on sets related to innovation and collected within the firm`s organizational or production systems. It is also important to clarify that, according to the indication of the authors of this study, the results of the data-driven framework can be compared among different companies. However, to collect data from different companies through the same questionnaire shall be avoided because the quality of the results is highly dependent on the homogeneity of groups` mindsets and perceptions.

Standardized, collaborative decision-making processes have already been implemented at some network-relevant airports, and these can be further enhanced through data-driven approaches (e.g., data analytics, predictions). New cost-effective implementations will also enable the appropriate integration of small and medium-sized airports into the aviation network. The required data can increasingly be gathered and processed by the airports themselves. For example, Automatic Dependent Surveillance-Broadcast (ADS-B) messages are sent by arriving and departing aircraft and enable a data-driven analysis of aircraft movements, taking into account local constraints (e.g., weather or capacity). Analytical and model-based approaches that leverage these data also offer deeper insights into the complex and interdependent airport operations. This includes systematic monitoring of relevant operational milestones as well as a corresponding predictive analysis to estimate future system states. In fact, local ADS-B receivers can be purchased, installed, and maintained at low cost, providing both very good coverage of the airport apron operations (runway, taxi system, parking positions) and communication of current airport performance to the network management. To prevent every small and medium-sized airport from having to develop its own monitoring system, we present a basic concept with our approach. We demonstrate that appropriate processing of ADS-B messages leads to improved situational awareness. Our concept is aligned with the operational milestones of Eurocontrol’s Airport Collaborative Decision Making (A-CDM) framework. Therefore, we analyze the A-CDM airport London–Gatwick Airport as it allows us to validate our concept against the data from the A-CDM implementation at a later stage. Finally, with our research, we also make a decisive contribution to the open-data and scientific community.