Yes / In view of the burgeoning scholarly works on big data and big data analytical capabilities, there remains limited research on how different access to big data and different big data analytic capabilities possessed by firms can generate diverse conditions leading to business failure. To fill this gap in the existing literature, an integrated framework was developed that entailed two approaches to big data as an asset (i.e. threshold resource and distinctive resource) and two types of competences in big data analytics (i.e. threshold competence and distinctive/core competence). The analysis provides insights into how ordinary big data analytic capability and mere possession of big data are more likely to create conditions for business failure. The study extends the existing streams of research by shedding light on decisions and processes in facilitating or hampering firms’ ability to harness big data to mitigate the cause of business failures. The analysis led to the categorization of a number of fruitful avenues for research on data-driven approaches to business failure.

Yes / The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

Yes / Purpose This study aims to understand pumpkin waste awareness among people by converting unstructured quantitative data into insightful information to understand the public's awareness of pumpkin waste during Halloween. Design/methodology/approach To fulfil the study's purpose, we extracted Halloween-related tweets by employing #halloween and #pumpkin hashtags and then investigated Halloween-related tweets via a topic modelling approach, specifically Latent Dirichlet Allocation. The tweets were collected from the UK between October 25th and November 7th, 2020. The analysis was completed with 11,744 tweets. Findings The topic modelling results revealed that people are aware of the pumpkin waste during Halloween. Furthermore, people tweet to reduce pumpkin waste by sharing recipes for using leftover pumpkins. Originality/value The study offers a novel approach to convert social media data into meaningful knowledge about public perception of food waste. This paper contributes to food waste literature by revealing people's awareness of pumpkin waste during Halloween using social media analytics. Norm activation model and communicative ecology theory are used for the theoretical underpinning of topic modelling.

No / This book provides a review of the state of the art in computer graphics, visualization, and visual analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in visualization and visual analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of visual, multimedia, and multimodal user interfaces, visual analysis at extreme scale, and adaptive visualization.

Sports analytics arrived in the mainstream media through the novel and film Moneyball. However, its origins date back to operations researchers following World War II. Often considered a subdiscipline of statistics, sports analytics draws from statistics but also includes concepts from data science, communication, and marketing. As a passionate fan of sports, I have pursued statistics in my undergraduate and graduate education with the dream of working in sports for my career. However, educational opportunities in sports analytics are limited nationwide, and more specifically, there is no educational opportunity at my university, California Polytechnic State University in San Luis Obispo. This thesis investigates the sports analytics discipline, aiming to explain what sports analytics is, how it differs from statistics, how sports analytics is used in various organizations, what sports analysts do, and how sports analytics should be taught at the undergraduate level here at Cal Poly. To accomplish this, I have taken three online sports analytics courses, conducted interviews with professors of sports analytics and sports analysts of professional and college teams, done extensive online research and literature review, and gauged interest campus-wide in a potential sports analytics course. Ultimately, this thesis led me to conclude that sports analytics differs from statistics, and there should be a course in sports analytics at Cal Poly offered by the Statistics Department. Skills including SQL and Tableau, communication to various sports constituents, data collection and data management, machine learning methods such as classification trees and clustering, advanced statistical methods such as General Additive Models and spatial analysis, and visualization techniques are all prominent in sports analytics. Statistics students at Cal Poly do not gain a firm foundation in all of these ideas and could benefit from a course which teaches these skills. The significance of this work is that I have created a course proposal for a sports analytics course. If this course were to be adopted by the Statistics Department, students would learn essential skills to prepare them for a career in sports or any data related career. This work can advance sports analytics education and lead to the creation of other courses in the discipline down the line.

Von den frühen Tagen der Logfile-Analysen bis hin zur heutigen Verwendung von fortschrittlichen Tracking-Systemen wie Google Analytics hat sich die Nutzerverfolgung im Netz stetig weiterentwickelt. Doch während sie Websitebetreibern und Werbedienstleistern wertvolle Informationen liefert, wirft sie auch Fragen hinsichtlich des Privatsphäre- und Datenschutzes auf. Das Sammeln von persönlichen Daten und deren anschließende Verwendung ruft bei vielen Menschen Besorgnis hervor. Die Gesetzgeber reagieren darauf mit immer strengeren Datenschutzgesetzen, die das Aggregieren, Verarbeiten und Speichern von personenbezogenen Daten in der Webanalyse einschränken. Viele Unternehmen stehen daher vor der Herausforderung, ihre Tracking-Infrastruktur zu überdenken und an die Vorgaben anzupassen. Spätestens mit der bevorstehenden Abschaffung der sogenannten Third-Party-Cookies sind Websitebetreiber gezwungen, aktiv zu werden. Diese Arbeit zielt darauf ab, Tracking-Technologien und -Strategien hinsichtlich ihrer Zukunftssicherheit zu analysieren, um einen Kompromiss zwischen den Interessen der Gesetzgebung und den Anbietern sowie Nutzern von Tracking-Tools zu finden.:Inhaltsverzeichnis Abkürzungsverzeichnis 1. Einleitung 2. Datenschutzrechtliche Rahmenbedingungen 2.1 Geschichte des Datenschutzrechts 2.2 Die DSGVO: Auswirkungen und Grundsätze 2.3 Rechtliche Einordnung von Tracking-Technologien 3. Grundlagen des Webtrackings 3.1 Cookies 3.1.1 Funktionsweise 3.1.2 Unterscheidung nach Lebensdauer 3.1.3 Unterscheidung nach Quelle 3.1.4 Unterscheidung nach Nutzungsart 3.1.5 Third-Party-Cookies in der Kritik 3.2 Tracking-Pixel 3.3 Device Fingerprinting 3.4 Datenqualität in der Krise 4. Tracking-Strategien in der Praxis 4.1 CNAME-Cloaking 4.1.1 Implementierung 4.1.2 Risiken 4.1.3 Datenschutzrechtliche Einordnung 4.2 Server Side Tracking 4.2.1 Tagging mit dem Google Tag Manager 4.2.2 Risiken 4.2.3 Datenschutzrechtliche Einordnung 4.3 Shynet 4.3.1 Implementierung und Quellcode-Analyse 4.3.2 Risiken 4.3.3 Datenschutzrechtliche Einordnung 5. Status Quo und Ausblick 5.1 Google Consent Mode v2 5.2 Browseranbieter 5.3 Cookie Pledge 5.4 E-Privacy-Verordnung 6. Fazit Literaturverzeichnis Abbildungsverzeichnis Eigenständigkeitserklärung

Sensemaking is the way in which we understand the world around us. Pirolli and Card developed a sensemaking model related to intelligence analysis, which involves taking raw, unstructured data, analyzing it, and presenting a report of the findings. With lower-cost immersive technologies becoming more popular, new opportunities exist to leverage embodied and distributed cognition to better support sensemaking by providing vast, immersive space for creating meaningful schemas (organizational structures) during an analysis task. This work builds on prior work in immersive analytics on the concept of Immersive Space to Think (IST), which provides analysts with immersive space to physically navigate and use to organize information during a sensemaking task. In this work, we performed several studies that aimed to understand how IST supports sensemaking and how we can develop additional features to better aid analysts while they complete sensemaking in immersive analytics systems, focusing on non-quantitative data analysis. In a series of exploratory user studies, we aimed to understand how users' sensemaking process evolves during multiple session analyses, which identified how the participants refined their use of the immersive space into later stages of the sensemaking process. Another exploratory user study highlighted how professional analysts and novice users share many similarities in immersive analytic tool usage during sensemaking within IST. In addition to looking at multi-session analysis tasks, we also explored how sensemaking strategies change as users become more familiar with the immersive analytics tool usage in an exploratory study that utilized multiple analysis tasks completed over a series of three user study sessions. Lastly, we conducted a comparative user study to evaluate how the addition of new organizational features, clustering, and linking affect sensemaking within IST. Overall, our studies expanded the IST tool set and gathered an enhanced understanding of how immersive space is utilized during analysis tasks within IST. / Doctor of Philosophy / Sensemaking is a process we do in our daily lives. It is how we understand the world around us, make decisions, and complete complex analyses, like journalists writing stories or detectives solving cases. Sensemaking involves gathering information, making sense of it, developing hypotheses, and drawing conclusions, similar to writing a report. This work builds on prior work in Immersive Space to Think (IST), which is a concept of using immersive technologies (Virtual /Augmented Reality) to support sensemaking by providing vast 3D space for organizing the data used in a sensemaking task. Additionally, using these technologies to support sensemaking provides benefits such as increased space for analysis, increased engagement, and natural user interaction, which allow us to interact with information used during sensemaking tasks in new ways. In IST, users are able to move virtual documents around in the space around them to support their analysis process. In this work, we ran a study focused on multi-session analysis within IST, revealing how users refined their document placements over time while completing sensemaking tasks within IST. We also ran a study to understand how professional analysts' and novice users' analysis with IST differed in the IST tool usage. In another user study, we explored how users' strategies for sensemaking and document layouts changed as they became more familiar with the IST tool. Lastly, we conducted a comparative user study to evaluate how new features like clustering and linking affected analysis within IST. Overall, our work contributed to an enhanced understanding of how immersive space is utilized during analysis tasks within IST.

A la différence de l'apprentissage par mémorisation (rote learning), l'apprentissage significatif (meaningful learning) vise à associer de nouvelles connaissances à des connaissances déjà acquises. La construction de cartes mentales exige et supporte la mise en place de stratégies d'apprentissage significatif, et permet de rendre visible la structure de connaissances de l'apprenant. Cependant, les enseignants qui intègrent la construction de cartes mentales dans leurs activités pédagogiques doivent se contenter du rendu final des cartes mentales, et risquent de faire de mauvaises interprétations et évaluations de celles-ci par manque d'information sur leur processus de construction. Dans cette thèse, nous nous intéressons à l'observation a posteriori de l'engagement de l'apprenant dans ses dimensions comportementale et cognitive, afin de proposer des indicateurs orientés processus qui permettent de comprendre ses actions et ses choix de construction de carte mentale. Nous avons suivi la méthodologie de recherche orientée par la conception (Design Based Research), qui nous a permis de proposer 3 niveaux de contributions : 1) un modèle théorique d'engagement (comportemental et cognitive) pour les activités de construction de cartes mentales, 2) un ensemble d'indicateurs d'engagement de l'apprenant à partir de traces capturées automatiquement lors de la construction de cartes mentales, et 3) un tableau de bord appelé MindMap Monitor présentant différents indicateurs à l'enseignant pour le suivi de la classe et des apprenants. Le modèle a été obtenu à partir d'une étude de littérature sur les théories de l'engagement de l'apprenant issues notamment de la recherche en psychologie de l'éducation. Les indicateurs ont été définis en croisant le modèle avec les résultats de plusieurs études de terrain avec les enseignants. Le tableau de bord implémentant les indicateurs a été construit en trois itérations. Son interface présente des vues synthétiques permettant de comparer les élèves de la classe et d'identifier ceux en difficulté lors de la construction de leur carte, et des vues détaillées décrivant l'activité de construction d'une carte pour chaque élève. Notre tableau de bord a été évalué au cours d'une expérimentation avec 12 enseignants en comparant son utilisation avec celle des cartes mentales finales associées aux vidéos de leur processus de construction. Les résultats montrent que nos indicateurs sur MindMap Monitor permettent de mieux identifier les élèves en difficulté, les difficultés partagées, ainsi que les difficultés pour un élève. Les résultats concernant la compréhension du processus de construction de la carte mentale sont plus nuancés. Nous avons également pu identifier plusieurs pistes d'amélioration sur le contenu du tableau de bord et sa présentation. Les perspectives de notre travail concernent principalement le suivi de l'engagement des apprenants en temps réel pour l'intervention et l'adaptation de la stratégie pédagogique / In contrast to rote learning, meaningful learning aims to associate new knowledge with knowledge already acquired. Mind mapping activities require and support the implementation of meaningful learning strategies and enlighten the knowledge structure of the learner. However, teachers who integrate mind mapping into their educational activities have to deal with the final rendering of maps, and risk to misinterpret and wrongly evaluate them due to the lack of information about their construction process. In this thesis, we are interested in a posteriori observation of the engagement of learners along its behavioral and cognitive dimensions, in order to propose processoriented indicators that help to understand actions and construction choices of mind maps. We followed the Design Based Research methodology, that allowed us to propose three levels of contributions : 1) a theoretical model of engagement (behavioral and cognitive) for mind mapping activities, 2) a set of indicators of learner engagement constructed from automatically captured map building traces, and 3) a dashboard called MindMap Monitor presenting various indicators to teachers for class and learners monitoring. The model was obtained from a literature review on theories of engagement, including research in educational psychology. The indicators have been defined by comparing the model with the results of several field studies with teachers. The dashboard implementing the indicators was developed following three iterations. Its interface presents synthetic views allowing the comparison of students in the class, the identification of those in difficulty, and detailed views describing the mind mapping activity for each student. Our dashboard was evaluated with an experiment involving 12 teachers. We compared its use with that of final mind maps associated with videos of their construction process. Results show that our indicators on MindMap Monitor are useful to better identify students in difficulty, shared difficulties, as well as difficulties for individual students. Results concerning the understanding of the mind maps construction process are more balanced. We were also able to identify several ways to improve both the content and the visualizations of the dashboard. The perspectives of our work are mainly related to monitoring learners’ engagement in real time for the intervention and adaptation of the teachers’ educational strategies

Maturity models have become a widely used framework for assessing various capabilities and technologies among businesses. This thesis develops a maturity model for assessing Business Analytics (BA) in Swedish e-commerce firms. Business Analytics has become an increasingly important part of modern businesses, and firms are continuously looking for new ways to perform analysis of the data available to them. The prominent previous maturity models within BA have mainly been developed by IT-consultancy firms with the underlying intent of selling their IT services. Consequently, these models have a primary focus on the technical factors towards Business Analytics maturity, partly neglecting the importance of organisational factors. This thesis develops a Business Analytic Maturity Model (BAMM) which fills an identified research gap of academic maturity models with emphasis on the organisational factors of BA maturity. Using a qualitative research design, the BAMM is adapted to the Swedish e-commerce industry through two sequential evaluation stages. The study finds that organisational factors have a greater impact on BA maturity than previous research suggests. The BAMM and the study's results contribute with knowledge of Business Analytics, as well as providing e-commerce firms with insights into how to leverage their data.

Studies are focused on methods for better recognition of internet shop customers. General this method is called Customer Intelligence. It contains means of increase of customers valuation for a society. It is based on exploitation of accumulated data about customer which helps to get necessary information. A main target of Studies is to create system of Customer Intelligence addapted to society needs. For internet seller it is necessary to include a web analytics into the system too. In a theoretical part there is an explanation of attitude and significance of Customer Intelligence in terms of informational company system. Furthermore there is taken a customer value. It is proceeded from basic means of customer valuation assesment via customers lifelong value for society to means of customer scoring A practical part of Studies is devoted to performed analyses of customer data which are the main details for execution of Customer Intelligence. There is explained how this obtained information will be used. In a conclusion there is descirbed a technical realization and problems which had to be necessarily solved during the implementation.