Problem discussion – we identified the gap concerning the lack of researches in exploring the impacts of both business and social networks in B2B companies’ internationalization process based on the network model proposed by Johanson and Mattsson (1988), especially taking the Chinese environmental factors into consideration. Purpose – The purpose of this study is to explore the importance of environmental factors and networks for B2B companies to internationalize into the Chinese market. Methodology – The thesis adopts the abductive approach and employs a qualitative research strategy. In order to obtain relevant information, multiple case study is chosen. The primary data are collected through semi-structured interviews with three Swedish B2B companies. Secondary data such as scientific articles, reports and books are also used in this thesis. Findings – Firstly, this thesis concludes that B2B companies’ business activities in internationalization process in China are influenced by four environmental factors: cultural, legal, economic and political factor. Secondly, business networks have impacts on the process of international extension, penetration and international integration, whereas the impacts of social networks are clearer in the phase of penetration. Thirdly, in China, there is no clear distinction between business- and social networks, but the building of trust is important in developing networks. Besides, B2B companies have to rely on their local employees when utilizing Guanxi in business activities. Managerial Implications – The managers should not only focus on the advantages, but also constantly perceive the risks in the Chinese market. Besides, managers could also take specific advantages of their Chinese employees’ social networks. Furthermore, managers should connect and coordinate the networks in different countries. Finally, managers could rely on their local employees to utilize Guanxi in business interactions. Theoretical Contribution – Firstly, this thesis identifies four key market environmental factors and their impacts for companies’ business activities in the Chinese market. Secondly, this thesis contributes to Johanson and Mattsson’s (1988) network approach by elaborating on the impacts of both business- and social networks in the three phases respectively. Thirdly, this thesis proposes a new conceptualized model that provides a deeper understanding and a clearer demonstration of the importance of environmental factors and networks when firms internationalize into global markets. Limitations – The results of this thesis might be limited to generalize to all kinds of market segments or industries. Moreover, in this thesis, the impacts of social networks are only identified in the phase of penetration. Keywords - Internationalization process, B2B companies, Environmental factor, Business network, Social network, Network approach, Impacts of network, the Chinese market

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Multiteam systems (MTSs), defined as two or more interdependent teams working towards both proximal team goals and at least one shared goal, are prevalent in modern organizations. Prior research has shown that MTS effectiveness is a function of the quality of both the processes occurring within each component team and between the teams in the system (Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005; DeChurch & Marks, 2006). The critical drivers of both team and MTS effectiveness include behavioral processes (explicit actions directed towards others; e.g., communication), cognitive states (knowledge or perceptions; e.g., transactive memory), and affective states (emotions or mood; e.g., stress) emerging from the shared experiences of the members of the team (Cohen & Bailey, 1997; Marks, Mathieu, & Zaccaro, 2001; Mathieu, Marks, & Zaccaro, 2001). While these phenomena exist both within and between teams, prior research has shown that such processes and states cannot be assumed equivalent across these levels (DeChurch & Zaccaro, 2010). Further complicating these relationships, these processes and states are expected to impact the relationships that other phenomena have on performance in addition to their expected direct effects (Ilgen, Hollenbeck, Johnson, & Jundt, 2005). With this, the purpose of this thesis is to study the relationships between process, cognitive and affective states, and performance as each exists within and between teams. Central to this purpose is examining the effects of cognitive and affective states on the relationship between process and performance. These relationships were tested using a laboratory sample of six-person MTSs (N = 118, n = 708) performing an action- and information sharing-oriented task. Utilizing network analysis, the direct and conditional impact of behavioral process (i.e., communication), cognitive states (i.e., advice relationships), and affective states (i.e., hindrance relationships) within and between teams were captured. It was found that the impact of between-team communication on MTS performance was moderated by between-team advice relationships and the impact of within-team communication on team performance was moderated by within-team hindrance relationships. Together, these findings suggest a need to consider the effects of within- and between-team processes on performance as having different conditional relationships with co-occurring states.

The aim of this thesis work is to investigate the algorithm of speech emotion recognition using MATLAB. Firstly, five most commonly used features are selected and extracted from speech signal. After this, statistical values such as mean, variance will be derived from the features. These data along with their related emotion target will be fed to MATLAB neural network tool to train and test to make up the classifier. The overall system provides a reliable performance, classifying correctly more than 82% speech samples after properly training.

Il presente studio si inserisce nel quadro di quel prolifico filone di studi che ormai da anni cerca di indagare il mondo dei network, analizzandone le ricadute organizzative, sia in termini di profitto, sia in termini di accrescimento della conoscenza organizzativa. L’idea di fondo, già ampiamente dibattuta, è che il network contribuisca efficacemente al miglioramento delle performance aziendali, generando fiducia e conoscenza attraverso una varietà di alleanze (orizzontali, verticali, trasversali…) e di legami (forti, deboli). In particolare, e questo è il focus principale del lavoro, ciò che ho voluto sottolineare è il ruolo particolare che le attività di networking possono esercitare durante le fasi di start up, quando cioè l’azienda prende forma e si costituisce: è durante questa fase che l’esistenza o meno di alleanze costituisce infatti un chiaro elemento differenziante, contribuendo al successo di quelle imprese che più di altre trovano nel sistema delle reti un valido ed efficace supporto al loro sviluppo. In sintesi, l’obiettivo di quest’analisi è stato quello di evidenziare l’impatto del networking, sia sugli asset finanziari, sia sulla generazione di nuove conoscenze e quindi sull’innovazione. In particolare, recuperando il concetto di learning economy, si è cercato di dimostrare il forte legame che esiste tra conoscenza e innovazione, soffermandosi in special modo sul cosiddetto “effetto spillover”. / This research is embedded in a prolific strand of studies investigating the world of networks with the aim to understand how they can generate organisational benefits, both in terms of profit and organisational knowledge. The driving idea, already extensively discussed, is that network-based organisations can achieve better performances, generating trust and knowledge through a variety of alliances (horizontal, vertical, transversal…) and ties (strong, weak…). In particular, aim of the present research is to stress the networking contribution to the start-up phase, when firms are established. Indeed it is during this phase that the existence or not of alliances makes the difference, contributing to the success of firms operating in a network-based system and recognizing its important contribution to the business results. In synthesis, goal of this analysis was to highlight the impact of networking activities, either on the financial assets or on the generation of innovation through the spread of new ideas. In particular, resorting to the concept of “learning economy”, I attempted to demonstrate the strong ties existing between knowledge and innovation, with a particular emphasis on the so-called “spillover effect”.

Cognitive radio networks have recently been proposed as a promising approach to overcome the serious problem of spectrum scarcity. Other emerging concept for innovative spectrum utilization is femtocells. Femtocells are low-power and short-range wireless access points installed by the end-user in residential or enterprise environments. A common feature of cognitive radio and femtocells is their two-tier nature involving primary and secondary users (PUs, SUs). While this new paradigm enables innovative alternatives to conventional spectrum management and utilization, it also brings its own technical challenges. A main challenge in cognitive radio is the design of efficient resource (spectrum) trading methods. Game and microeconomics theories provide tools for studying the strategic interactions through rationality and economic benefits between PUs and SUs for effective resource allocation. In this thesis, we investigate some efficient game theoretic and microeconomic approaches to address spectrum trading in cognitive networks. We propose two auction frameworks for shared and exclusive use models. In the first auction mechanism, we consider the shared used model in cognitive radio networks and design a spectrum trading method to maximize the total satisfaction of the SUs and revenue of the Wireless Service Provider (WSP). In the second auction mechanism, we investigate spectrum trading via auction approach for exclusive usage spectrum access model in cognitive radio networks. We consider a realistic valuation function and propose an efficient concurrent Vickrey-Clarke-Grove (VCG) mechanism for non-identical channel allocation among r-minded bidders in two different cases. The realization of cognitive radio networks in practice requires the development of effective spectrum sensing methods. A fundamental question is how much time to allocate for sensing purposes. In the literature on cognitive radio, it is commonly assumed that fixed time durations are assigned for spectrum sensing and data transmission. It is however possible to improve the network performance by finding the best tradeoff between sensing time and throughput. In this thesis, we derive an expression for the total average throughput of the SUs over time-varying fading channels. Then we maximize the total average throughput in terms of sensing time and the number of SUs assigned to cooperatively sense each channel. For practical implementation, we propose a dynamical programming algorithm for joint optimization of sensing time and the number of cooperating SUs for sensing purpose. Simulation results demonstrate that significant improvement in the throughput of SUs is achieved in the case of joint optimization. In the last part of the thesis, we further address the challenge of pricing in oligopoly market for open access femtocell networks. We propose dynamic pricing schemes based on microeconomic and game theoretic approaches such as market equilibrium, Bertrand game, multiple-leader-multiple-follower Stackelberg game. Based on our approaches, the per unit price of spectrum can be determined dynamically and mobile service providers can gain more revenue than fixed pricing scheme. Our proposed methods also provide residential customers more incentives and satisfaction to participate in open access model.

An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability.

Mobile Ad-hoc Wireless Networks (MANETs) present a new paradigm in which to realize a variety of communication technologies and services. The use of stochastic event-based simulation is a common approach to modelling MANET operations as part of the engineering process. To improve observations many simulations are often averaged together to produce estimations of MANET operation; however, to be statistically meaningful start-up transients must be removed, and only ergodic data averaged. These statistical issues of stationarity and ergodicity are often approached in an ad-hoc manner, if at all. This thesis presents a formal method to address these two statistical issues and applies it to the problem of quantifying MANET operation under different physical-layer jamming strategies. This demonstration illustrates the complex nature of MANET operation and the need for rigorous statistical analysis as part of the engineering process. / Graduate

Future electrical power networks should support the integration of distributed renewable energy sources, which may be contributed by individual customers instead of utility companies. Such a demand poses new challenges to power distribution network design, since the energy generation, energy consumption, and power flow all become highly dynamic. An inappropriate network design may not only waste much energy in power distribution but also incur high cost in network construction. In this thesis, we study the optimal network design problem under a dynamic current injection model. We investigate different optimization methods to obtain the optimal network structure that can better adapt to dynamic energy generation/consumption requirements and is more efficient than traditional tree-structured power networks. By predicting users' potential load in the network, network design with our method results in significant energy saving. / Graduate / 0984

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