La tesi ha l’obiettivo di analizzare il cambiamento organizzativo in una prospettiva di social network analysis, sfruttando dati longitudinali raccolti a seguito della modifica della struttura organizzativa in un Policlinico Universitario italiano. Il manoscritto è organizzativo in tre paper. Il primo paper si focalizza sul tema del rapporto tra network formali e network informali, analizzando come la modifica del primo comporti una corrispondente variazione nel secondo. Il paper dimostra come, in assenza di strutture organizzative ben formalizzate, gli individui tendono ad allacciare nuovi legami con colleghi che appartengono alla stessa specializzazione. Il secondo paper, invece, attingendo prettamente alla letteratura di comportamento organizzativo, analizza il tema della dinamicità del network, fornendo evidenze in relazione alla stabilità del network stesso a seguito del cambiamento. Particolare attenzione, è inoltre, dedicata alle dinamiche intra – team e al ruolo di quest’ultime nell’accettazione o meno del cambiamento. Infine, il terzo paper sviluppa il tema della network density e di come quest’ultima possa essere correlato al cambiamento organizzativo, in termini di reazione al cambiamento. Inoltre, si dimostra come la formalizzazione abbia un impatto positivo sulla densità del network, specie in contesti organizzativi caratterizzati da una bassa gerarchia e coordinamento orizzontale. / This thesis aims to analyze organizational change in a social network analysis perspective, exploiting longitudinal data collected after a modification of the organizational structure in an Italian Teaching Hospital The manuscript is organized into three papers. The first paper focuses on the theme of the relationship between formal networks and informal networks, analyzing how the modification of the first involves a corresponding variation in the second. The paper demonstrates how, in the absence of formalized organizational structures, individuals tend to establish new ties with colleagues who belong to the same specialization. The second paper, drawing purely from the organizational behavior literature, analyzes the issue of the network dynamics , providing evidence and antecedents for network stability in response to organizational change. Particular attention is also given to the intra - team dynamics and the impact of individual perception of collective properties in driving employees in accepting or not the organizational change. Finally, the third paper develops the theme of network density and how the latter can be related to organizational change, in terms of reaction to change. Furthermore, it is shown how formalization has a positive impact on network density, especially in organizational contexts characterized by a low hierarchy and horizontal coordination.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)