Social media has become an important means of everyday communication. It is a mechanism for “sharing” and “resharing” of information. While social network platforms provide the means to users for resharing/reblogging (aka retweeting), it remains unclear what motivates users to share. Predicting the spread of content is quite important for several purposes such as viral marketing, popular news detection, personalized message recommendation and on-line advertisement. Social content systems store all the information produced in the interactions between users. However, to turn this data into information that allows us to extract patterns, it is important to consider the different phenomena involved in these interactions. In this work, two phenomena that influence the evolution of networks are studied for Twitter: diffusion of information and communication among users. Previous studies have shown that history of interaction among users and properties of the message are good attributes to understand the retweet behavior of users. Factors like content of message and time are less investigated. We propose a prediction model for retweet actions of users. It formulates a function which ranks the users according to how receptive they are to a particular message. The function generates a confidence score for the edges joining the initiator of the message and the followers. Two different pieces of information propagate through different users in the network. We divide the task of calculating confidence score into two parts. The first part is independent of the test tweet. It models transmission rate of the tie between the initiator and the follower. We call this as ‘Pairwise Influence Estimation’. The second part incorporates the tweet properties and user activeness as per time in the ranking function. The proposed model exploits all the dimensions of information dif-fusion process-influence, content and temporal properties. We have captured local aspects of diffusion. It has been observed that users do not read all the messages on their site. This results in shortcomings in the above models. Considering this, we first study the temporal behavior of users’ activities, which directly reflects their availability pertaining to the upcoming post. Also, as it is a continuous task of predicting retweet behavior, we design a user-centric, and temporally localized incremental classification model by considering the fact that users do not read all their tweets. We have tested the effectiveness of this model by using real data from Twitter. We demonstrate that the new proposed model is more accurate in describing the information propagation in microblog compared to the existing methods. Our model works well when we consider different classes of users depending on their activity patterns. In addition, we also investigate the parameters of the model for different classes of users. We report some interesting distinguishing patterns in retweeting behavior of users.

The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.

Au vu de la prévalence significative des événements indésirables liés aux médicaments, ainsi que du risque croissant de résistance aux antibiotiques (causée principalement par les prescriptions inappropriées et une utilisation excessive), nous proposons une architecture générale pour des systèmes de recommandation adaptés à ce type de contexte et nous en développons un pour la prescription d’antibiotiques (PARS). Le type de contexte pour lequel l’approche est proposée est caractérisé par des décisions à haut risque et/ou à enjeux importants. Le système ne peut être basé sur l’apprentissage car une base de données de cas n’est pas disponible. Toutefois, des connaissances et des règles de bonnes pratiques existent et de ce fait il convient de développer un système capable de les modéliser et de les mettre en oeuvre. Le système est destiné à un utilisateur qui est le décideur qui doit adapter sa décision à chaque sujet dont les besoins et les caractéristiques sont spécifiques. Le modèle doit pouvoir s’adapter à différents types d’évolutions. L’approche est basée sur la combinaison des technologies web sémantique avec un modèle d’aide multicritère à la décision. Le système comporte deux étapes. Compte tenu de la spécificité du domaine d’application, l’approche évalue d’abord la pertinence d’une alternative (action) pour un sujet et un besoin donnés dans un contexte spécifique. Le premier niveau du modèle d’aide à la décision est de sélectionner selon le besoin l’ensemble des alternatives qui ont le potentiel d’être appropriées. Le deuxième niveau consiste à évaluer et à trier les alternatives dans des catégories en fonction de leur adéquation. Nous proposons une approche qui exploite les schémas de connaissances du web sémantiques (ontologies) et qui structure les règles de recommandation en une méthode de tri adaptée : MR-Sort avec Veto. Cette approche permet de lier et de mettre en correspondance des sources de connaissances hétérogènes exprimées par des experts. En collaboration avec le Centre hospitalier EpiCURA, nous avons appliqué cette approche dans le domaine médical et plus précisément, pour la prescription des antibiotiques. Les performances de l’approche ont été comparées aux recommandations données par EpiCURA. Les résultats ont montré que le système proposé est plus détaillé dans ses recommandations par comparaison aux guidelines en usage au Centre EpiCURA. En prenant éventuellement en compte des caractéristiques supplémentaires des sujets, le modèle est capable de s’adapter à des changements dans le contexte (nouveaux antibiotiques, effets secondaires, développement de germes résistants). / Motivated by the well documented worldwide spread of adverse drug events that are associated to antibiotics usage, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a general architecture for recommendation systems adapted for this kind of context and we develop a specific system for antibiotic prescription (PARS). The type of context that our architecture covers is characterised by highly risky decisions or decisions with high stakes. Such a system cannot be based on machine learning, since there are no available training data sets or case bases. However, rules of good practice and expert knowledge are available, therefore our system should be able to model and implement them. The proposed solution is intended to be used by a decision maker who must adapt his/her decision both to each subject’s specific needs and characteristics, as well as to different types of evolution. Our approach is based on the combination of semantic technologies with MCDA (Multi-Criteria Decision Aids). The decision support process involves two steps. First, by taking into account the specific application domain, the approach evaluates the relevance of each alternative (action) in order to satisfy the needs of a given subject. The first level of the decision support model aims to select all the alternatives that have the potential to fulfill the subject’s needs. Subsequently, the second level consists of evaluating and sorting the selected alternatives in categories according to their adequacy to the characteristics of the subject. We propose an approach that exploits the knowledge schemes of semantic web technologies (ontologies) and that structures the recommendation rules into a suitable sorting method: the MR-Sort with Veto. By doing so, our solution is able to link and match heterogeneous knowledge sources expressed by experts. In collaboration with the EpiCURA Hospital Center, we have applied this approach in the medical domain and more specifically in the prescription of antibiotics. The system’s recommendations were compared with those expressed in the guidelines currently in use at EpiCURA. The results showed us that PARS allows for a better consideration of the sensitivity of the patients to the adverse effects of antibiotics. Moreover, by taking into account the additional characteristics of the patients, the model is able to adapt to contextual changes (such as new antibiotics, side effects and development of resistant micro-organisms).

Background: To reach the global goals of 2030, changes need to be maderegarding how decision makers decide to freight their goods. In Sweden,road traffic is held accountable for 90% of the carbon dioxide emissions.To help solve the problem with vast distances in Sweden's rural areas,scientists have researched the possibility of coordinating the transports.The research has shown that transports receive a more appropriate fill rate,lower transportation costs, and because of those two factors, lowered theircarbon dioxide emissions. Purpose: The purpose of this study is to lay a scientific foundation andpresent an example of a decision model. The decision model mainly aimstowards decision makers in rural municipalities but also companies thatwork toward these rural areas.  Methodology: This research undertook a qualitative research approach inan exploratory method. In order to ensure that the criteria’s and the prosand cons with coordinating freight transports the researchers interviewedkey persons for the rural municipalities in a semi structured or structurednature. Findings: The study has found the most relevant pros and cons with acoordination of freight transports and the criteria’s needed to build anexample of a decision model.  Conclusions: The theoretical and empirical data has been similar in many,but one stands out, and that is the increase of lead times as a negativefactor the rural areas, because of the coordination. The citizens andcompanies these rural areas are more interested in consistency andreliability. The study concluded the necessary criteria’s needed to build thedecision model. / Bakgrund: För att Sverige ska kunna nå sina uppsatta klimatmål innan år2030, behövs stora förändringar ske i hur vi skickar och transporterar vårtgods. Idag står Sveriges vägtrafik för 90% av de koldioxidutsläpp somsker av alla inrikes transporter. För att lösa problemet med de långaavstånden ut mot glesbygden har man undersökt möjligheten till attförsöka samordna varutransporterna som ska till glesbygdsområden.Tidigare varusamordningsprojekt har visat sig tillföra starka fördelar förtransportföretag som bland annat bättre fyllnadsgrad och reduceradetransportkostnader, samtidigt som att man minskar utsläppen kopplade tilltransporter. Syfte: Studiens syfte är att lägga en vetenskaplig grund förvarusamordning samt presentera en beslutsmodell. Beslutsmodellen riktarsig mot kommunala beslutsfattare samt beslutsfattare inom företag somjobbar mot glesbygder. Metod: Studien har utförts enligt ramverket för en kvalitativ intervjustudiemed en explorativ forskningsdesign. Detta för att författarna ska ha frihetatt arbeta passande efter situationen då det är ett relativt outforskat ämne. Analys och resultat: I studiens resultat presenteras delsvarusamordningens olika fördelar, dels dess nackdelar. Dessa för- ochnackdelar är tänkta att synliggöra varusamordningens möjligheter samttillkortakommanden.Vidare presenteras en beslutsmodell med förslag på beslutskriterier somtagits fram från de teoretiska och empiriska information som insamladestill studien.

There are many possible tools to develop mobile applications with. Choosing a development tool is done by considering many different factors, and the choice is currently done, in many cases, arbitrarily. For this project, a decision model is designed to ease the process of choosing a development tool. A survey was conducted to examine how people using different smartphone platforms discover and download applications. 94 responses were collected, showing that approximately 50% of Android-users found mobile applications by using search engines or browsers. The corresponding number was approximately 30% for iOS-users. A usability test was conducted to discover the differences in user experience between Progressive Web Applications and native applications. 18 usability tests were conducted comparing the same product developed as a Progressive Web Application and a native application. A majority of the participants had a technical background. Both Android and iOS devices were included in the tests. The results indicated that end-users notice when an application is not natively developed. The effect on the user experience is combined with other technical differences and applied to the decision model. This model was designed to predict if a native application, a Progressive Web Application or a React Native application is the most favourable to develop for a specific scenario. The final model could, according to consultants at the stakeholder Slagkryssaren AB, with good accuracy predict when the different development tools should be used. The model could be used as a discussion tool in the first stages of the development process of an application. / Det finns många möjliga verktyg för att utveckla mobila applikationer. Valet av utvecklingsverktyg görs genom att överväga många olika faktorer, och görs idag i många fall högst godtyckligt. För det här projektet designades en beslutsmodell som förenklar processen av att välja ett utecklingsverktyg. En undersökning gjordes för att undersöka hur användare av olika smartphone- plattformar upptäcker och laddar ner applikationer. 94 svar samlades, svaren visade att ungefär 50% av Android-användare hittade mobila applikationer genom internetsökningar eller webbläsare. Denna siffran var ungefär 30% för iOS-användare. Ett användarbarhetstest utfördes för att finna skillnader i användarupplevelse mellan progressiva webbapplikationer och native-applikationer. En majoritet av deltagarna hade en teknisk bakgrund. Både Android- och iOS-enheter testades. Resultatet tydde på att slutanvändare la märke till när en applikation inte utvecklades som en native-applikation. Effekten på användarvänligheten, kombinerat med tekniska skillnader mellan verktygen, tillämpades på beslutsmodellen. Modellen designades för att förutse om en native-applikation, en progressiva webbapplikation eller en React Native- applikation är mest fördelaktig att utveckla i ett specifikt scenario. Den slutgiltiga modellen kunde, enligt konsulter på uppdragsgivaren Slagkryssaren AB, med god precision avgöra när de olika utvecklingsverktygen bör nyttjas. Modellens användning blev som ett diskussionsverktyg i de första stadierna av processen med att välja utvecklingsvektyg.

Die vorliegende Arbeit befasst sich mit Innovationsdiffusionsmodellen und deren Anwendung in der Marketingpraxis. Sie hat zwei Ziele: Einen Überblick über existierende Innovationsmodelle zu schaffen und ein neues besseres Modell zu entwickeln. Es wird ein neuer Klassifizierungsansatz vorgeschlagen, mit dessen Hilfe ein strukturierter Überblick über die vorhandenen zahlreichen Innovationsdiffusionsmodelle möglich wird. Die Klassifizierung beruht auf den Annahmen in den Innovationsdiffusionsmodellen. Dies erlaubt im Gegensatz zu den bekannten Klassifizierungen (z.B. von Roberts/Lattin (2000)) die Bildung von disjunkten Modellklassen. Anhand der neuen Klassifizierung werden die prominenten Modelle, wie z.B. Bass-Modell (1969) bzw. Kalish-Modell (1985) eingeordnet und ihre Vor- und Nachteile aufgezeigt. Dieser Ansatz erleichtert eine Entscheidung für das beste zu verwendende Modell, wenn bekannt ist, welche Daten (Absatzdaten, Daten über Konsumenten etc.) zur Verfügung stehen und/oder welches Ziel (Absatzprognose, Preisbestimmung) verfolgt wird. Im zweiten Teil der Arbeit wird ein neues hybrides Innovationsdiffusionsmodell – das Information-Disicion-Evaluation-Modell (IED-Modell) – vorgestellt. Das IED-Modell besitzt zahlreiche Vorteile gegenüber existierenden Innovationsdiffusionsmodellen. Die Struktur des IED-Modells ist sehr allgemein so, dass das IED-Modell als eine Modellklasse bezeichnet werden könnte. Werden die Annahmen des IED-Modells genau definiert (z.B. über die Anzahl der Wettbewerbsprodukte usw.), erhält es eine explizite Form, die prominenten Innovationsdiffusionsmodellen ähnlich oder vollkommen identisch sein kann (für die Erstellung einer expliziten Form des IED-Modells siehe www.ied-modell.de). Ein solcher allgemeiner Modellierungsansatz des IED-Modells ist neu für die Innovationsdiffusionsforschung. Das IED-Modell und dessen Annahmen werden mittels Monte-Carlo-Simulationen analysiert. Beim empirischen Test an realen Daten wird das IED-Modell mit vier renommierten Innovationsdiffusionsmodellen verglichen. Laut diesem Vergleich ist das Anpassungsvermögen des IED-Modells im Durchschnitt besser als das der vier Vergleichsmodelle. Bei drei- und zehnmonatlichen Prognosen zeigte das IED-Modell eine sehr gute Vorhersagefähigkeit. / This work assesses innovation diffusion models and their application in marketing management. Its two principal aims are: (1) to give an overview of existing innovation diffusion models and (2) to develop a new and improved model. A new classification approach is proposed. The classification methodology bases on typical assumptions made in innovation diffusion models. Unlike prior classifications, e.g. Roberts/Lattin (2000), this approach allows for disjunctive classes. By means of this classification renowned models like Bass Model (1969) or Kalish Model (1985) are categorized, and their advantages and disadvantages are analyzed. This helps decide which model should be used depending on data availability (sales data, consumer data etc.) and the overall goal of a model investigation (sales forecast, pricing etc.). In the second part of this work the new hybrid innovation diffusion model – Innovation-Decision-Evaluation model (IED model) – is described. The model has several advantages compared with existing models. The structure of the IED Model is non-specific so that the IED model can be described as a distinct model class. When assumptions of the IED model are specified (e.g. number of competitive products) the model gets an explicit form which can be similar or even identical to other innovation diffusion models (for the design of an explicit model form see also www.ied-modell.de). Such a generalized modeling approach in IED modelling is new in innovation diffusion research. The IED model and its assumptions are analysed with Monte Carlo simulations. Its results are also empirically tested and compared with four renowned innovation diffusion models. The comparison reveals that the IED model has the best average fit and good forecast goodness.

La conduite de réformes présente un risque élevé pour les décideurs publics : les échecs sont lourds de conséquences pour les politiques publiques visées comme pour les responsables politiques qui les portent. Si le risque politique des réformes est reconnu comme un élément clé dans la prise de décision, sa définition reste floue pour les praticiens. Une revue de littérature en sciences de la décision, science politique et économie politique montre que ce concept est également dans un angle mort théorique. Sur le terrain des réformes, cette recherche vise à définir le risque politique comme la combinaison de facteurs de risque déclenchant des événements perturbateurs conduisant à un degré d'adoption plus ou moins élevé de la réforme projetée. Plus de quarante études de cas ont permis de dégager six facteurs de risque : les caractéristiques intrinsèques de la réforme, l’opinion publique, les parties prenantes, l’environnement politique, le processus de décision et le contexte socio-économique. Le concept de risque politique est ensuite opérationnalisé et testé grâce à des grilles de scores. Il en ressort qu'il existe des relations robustes entre les scores atteints sur les facteurs de risque, les événements perturbateurs et le degré d'adoption des réformes. Cette recherche est une contribution à une meilleure compréhension des interactions entre stratégie et politique dans la prise de décision, améliore la compréhension des ressorts de la prise de décision stratégique dans le secteur public et ouvre la voie à une approche de la conduite des réformes par la gestion des risques. / Carrying out reforms entails a high level of risk for policy makers: reform failure can have far-reaching consequences on both the public policy concerned and the reputation of the political leaders who are pushing for the reform. Policy makers widely acknowledge the role of “political risk” in public decision making. However, its definition remains vague. A literature review in decision sciences, political science and political economy shows that the concept of political risk is a blind spot in academic theory. This research project aims to develop a better understanding of the reasons why some reform initiatives fail while others succeed. It defines political risk as a combination of risk factors which contribute to trigger disruptive events and, in turn, influence the enactment of reforms. Six risk factors are identified on the basis of more than forty reform case studies: intrinsic characteristics of the reform, public opinion, stakeholders, political context and socio-economic context. The concept of political risk is then operationalized and tested using a scorecard approach. The tests show a consistent relation between risk factors, disruptive events and reform enactment. This project contributes to a better understanding of the link between strategy and politics in decision making and the dynamics of strategic decision making in the public sector. It paves the way for a risk based approach to steering public policy reforms.

The Master's thesis focuses on international expansion process of a company. For the application part of the thesis I have chosen a company called Žatec brewery Ltd. which belongs into a category of small and medium enterprises. The main objectives of the thesis comprise selection of a convenient Western Balkans region market for expansion of the brewery and subsequent formulation of a specific market entry strategy. The market entry strategy consists of a market entry mode selection, competitive strategy and marketing strategy as well as marketing mix 4P. The minor objectives include characteristics of the brewery's business strategy and the role of international expansion in it and also the company's financial analysis whose results underpine its expansion possibilities. The other minor objectives comprise multi-criteria decision model creation in order to decide for the most attractive target market and the expansion key success factors identification. As for the methods used, analyzes like financial analysis, PEST analysis, Porter's model of five forces and SWOT analysis, and then comparisons dominate. Croatia was finally identified as the most suitable regional market for the brewery's expansion and the company was advised to enter it with its gluten-free lager product. It was suggested, at the same time, that the brewery use to its advantage the background provided by its current owner - beer brewing company Carlsberg.

This dissertation set out to understand the decision processes used by decision makers in adversarial environment by setting up an adversarial decision making microworld, as an experimental platform, using a real time strategy (RTS) game called Rise of Nations (RON). The specific objectives of this dissertation were: 1.Contribute to the validation of recognition-primed decision (RPD) model in a simulated adversarial environment; 2.Explore the roles of common-sense strategies in decision making in the adversarial environment; and 3.Test the effectiveness of training recommendations based on the RPD model. Three related experimental studies were setup to investigate each of the objectives. Study 1 found that RPD model was partly valid where RPD processes were prevalently used but other decision processes were also important in an adversarial environment. A new decision model (ConPAD model) was proposed to capture the nature of decision making in the adversarial environment. It was also found that cognitive abilities might have some effects on the types of decision processes used by the decision makers. Study 2 found that common-sense strategies were prevalent in the adversarial environment where the participants were able to use all but one of the warfare related strategies extracted from literature without teaching them. The strategy familiarization training was not found to significantly improve decision making but showed that common-sense strategies were prevalent and simple familiarization training was not sufficient to produce differences in strategy usage and performances from the novice participants. Study 3 also found that RPD based training (cue-recognition and decision skill training) were not significant in producing better performance although subjective feedback found such training to be useful. However, the participants with RPD based training conditions were able to perform on the same level as the expert participants bridging the gap between novices and experts. Based on the findings, it was recommended that decision training should involve not just RPD based training, but comparisons of attributes as well. A more interactive training combining common-sense strategies, cue-recognition and decision skill training might be more useful. More theoretical experimentation would be required to validate the new decision model proposed in this dissertation. / Ph. D.