Business simulation games are used in educational institutions and various industries in the private and public sector to train students and employees to practice the principles of management and decision-making skills by providing a fail-safe environment and enabling them to reflect on their simulation results. These games are generally advanced multiuser environments where a user, or a group of users, have access to a virtual company for making business decisions. Some of these games are expensive and their licences are time limited; typically, such a licence is only valid during the duration of the course. In general, these games are not available for the public as part of informal instructional courses. In Canada, teaching informal courses to immigrants and refugees, which involve data-driven decision making, to prepare them for future challenges they might encounter as business owners, can be challenging; especially considering language barriers and non-business-related backgrounds obtained outside of Canada. Furthermore, based on their decision-making styles, cognitive limitations, and past experiences, people may have an inaccurate perception of the problem or challenge they face, this could lead to poor decision making of the team they are part of and, therefore, this could reflect in the effectiveness of an organization as a whole. The objective of this research is to enrich current teaching tools in decision-making processes in entrepreneurship courses for newcomers in Canada with a comprehensive and visual representation of operational business problems involved in Business Intelligence and data analytics. More specifically, we designed and developed a 3D Business Simulation Game with randomized scenarios using modern technologies, such as Unreal Engine as the game engine; Adobe Fuse for the character creation, Mixamo for animation of the character, and Substance Painter for textures and materials for the assets. The research was conducted with the participation of the students of the Business Creation and Project Management course at VIRCS (Victoria Immigrant and Refugee Centre Society) where we tested this game on each one of the five units of the course. After designing, developing, and testing the 3D business simulation game, we conducted a comprehensive evaluation to investigate whether the decisions students made while playing were correct or not. We also evaluated whether they felt that the challenges were easier to understand, both as a team and individually, when they used the 3D business simulation game compared to only the written description of the problems. The main results we obtained from our study are the following: After playing the business simulation game, students became more aware of the importance of making correct decisions in different business scenarios. They made sure that the whole team understood the problem, and they felt generally good about their understanding of the course content. We also noticed that when the animation was not part of the business simulation game, they seemed to be confused when following written instructions. This indicate that they depended on the animations for their decision-making. We believe that, in some ways, the course and the 3D business simulation game we created for this research were a great opportunity to observe students becoming more confident in their future in Canada as entrepreneurs. We observed that, once the game has been used, the students become more participatory in class, the discussion of the course material increases, and in general, the students seem to enjoy the course more. / Graduate

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De nos jours, il y a une demande croissante pour fournir de l'information temps réel à partir de l'environnement, e.g. état infectieux de maladies, force du signal, conditions de circulation, qualité de l'air. La prolifération des dispositifs de capteurs et la mobilité des personnes font de la Mobile Collaborative Sensing (MCS) un moyen efficace de détecter et collecter l'information à un faible coût. Dans MCS, au lieu de déployer des capteurs statiques dans une zone, les personnes disposant d'appareils mobiles jouent le rôle de capteurs mobiles. En général, une application MCS exige que l'appareil de chacun ait la capacité d'effectuer la détection et retourne les résultats à un serveur central, mais également de collaborer avec d'autres dispositifs. Pour que les résultats puissent représenter l'information physique d'une région cible et convenir, quel type de données peut être utilisé et quel type d'information doit être inclus dans les données collectées? Les données spatio-temporelles peuvent être utilisées par des applications pour bien représenter la région cible. Dans des applications différentes, l'information de localisation et de temps sont 2 types d'information communes, et en les utilisant la région cible d'une application est sous surveillance complète du temps et de l'espace. Différentes applications nécessitent de l'information différente pour atteindre des objectifs différents. E.g. dans cette thèse: i- MCS-Locating application: l'information de résistance du signal doit être incluse dans les données détectées par des dispositifs mobiles à partir d'émetteurs de signaux ; ii- MCS-Prédiction application : la relation entre les cas d'infection et les cas infectés doit être incluse dans les données par les dispositifs mobiles provenant des zones de flambée de la maladie ; iii- MCS-Routing application : l'information routière en temps réel provenant de différentes routes de circulation doit être incluse dans les données détectées par des dispositifs embarqués. Avec la détection de l'information physique d'une région cible, et la mise en interaction des dispositifs, 3 thèmes d'optimisation basés sur la détection sont étudiés et 4 travaux de recherche menés: -Mobile Collaboratif Détection Cadre : un cadre mobile de détection collaborative est conçu pour faciliter la coopérativité de la collecte, du partage et de l'analyse des données. Les données sont collectées à partir de sources et de points temporels différents. Pour le déploiement du cadre dans les applications, les défis clés pertinents et les problèmes ouverts sont discutés. -MCS-Locating : l'algorithme LiCS (Locating in Collaborative Sensing based Data Space) est proposé pour atteindre la localisation de la cible. LiCS utilise la puissance du signal reçu dans tous les périphériques sans fil comme empreintes digitales de localisation pour les différents emplacements. De sorte LiCS peut être directement pris en charge par l'infrastructure sans fil standard. Il utilise des données de trace d'appareils mobiles d'individus, et un modèle d'estimation d'emplacement. Il forme le modèle d'estimation de localisation en utilisant les données de trace pour atteindre la localisation de la cible collaborative. Cette collaboration entre périphériques est au niveau des données et est supportée par un modèle. -MCS-Prédiction: un modèle de reconnaissance est conçu pour acquérir dynamiquement la connaissance de structure de la RCN pertinente pendant la propagation de la maladie. Sur ce modèle, un algorithme de prédiction est proposé pour prédire le paramètre R. i.e. le nombre de reproduction qui est utilisé pour quantifier la dynamique de la maladie pendant sa propagation. -MCS-Routing : un algorithme de navigation écologique ‘eRouting’ est conçu en combinant l'information de trafic temps réel et un modèle d'énergie/émission basé sur des facteurs représentatifs. Sur la base de l'infrastructure standard d'un système de trafic intelligent, l'information sur le trafic est collectée / Nowadays, there is an increasing demand to provide real-time information from the environment, e.g., the infection status of infectious diseases, signal strength, traffic conditions, and air quality, to citizens in urban areas for various purposes. The proliferation of sensor-equipped devices and the mobility of people are making Mobile Collaborative Sensing (MCS) an effective way to sense and collect information at a low deployment cost. In MCS, instead of just deploying static sensors in an interested area, people with mobile devices play the role of mobile sensors to sense the information of their surroundings, and the communication network (3G, WiFi, etc.) is used to transfer data for MCS applications. Typically, a MCS application not only requires each participant's mobile device to possess the capability of performing sensing and returning sensed results to a central server, but also requires to collaborate with other mobile and static devices. In order to make sensed results well represent the physical information of a target region, and well be suitable to a certain application, what kind of data can be used for different applications, and what kind of information needs to be included into the collected sensing data? Spatio-temporal data can be used by different applications to well represent the target region. In different applications, location and time information is two kinds of common information, and by using such information, the target region of an application is under comprehensive monitoring from the view of time and space. Different applications require different information to achieve different sensing purposes. E.g. in this thesis: i- MCS-Locating application: signal strength information needs to be included into the sensed data by mobile devices from signal transmitters; ii- MCS-Prediction application: the relationship between infecting and infected cases needs to be included into the sensed data by mobile devices from disease outbreak areas; iii- MCS-Routing application: real-time traffic and road information from different traffic roads, e.g., traffic velocity and road gradient, needs to be included into the sensed data by road-embedded and vehicle-mounted devices. With sensing the physical information of a target region, and making mobile and static devices collaborate with each other in mind, in this thesis three sensing based optimization applications are studied, and following four research works are conducted: - a MCS Framework is designed to facilitate the cooperativity of data collection, sharing, and analysis among different devices. Data is collected from different sources and time points. For deploying the framework into applications, relevant key challenges and open issues are discussed. - MCS-Locating: an algorithm LiCS (Locating in Collaborative Sensing based Data Space) is proposed to achieve target locating. It uses Received Signal Strength that exists in any wireless devices as location fingerprints to differentiate different locations, so it can be directly supported by off-the-shelf wireless infrastructure. LiCS uses trace data from individuals' mobile devices, and a location estimation model. It trains the location estimation model by using the trace data to achieve collaborative target locating. Such collaboration between different devices is data-level, and model-supported. - MCS-Prediction: a recognition model is designed to dynamically acquire the structure knowledge of the relevant RCN during disease spread. On the basis of this model, a prediction algorithm is proposed to predict the parameter R. R is the reproductive number which is used to quantify the disease dynamics during disease spread. - MCS-Routing: an eco-friendly navigation algorithm, eRouting, is designed by combining real-time traffic information and a representative factor based energy/emission model. Based on the off-the-shelf infrastructure of an intelligent traffic system, the traffic information is collected

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No description available.

Big Data has been among the most innovative topics in literature sources and among organizations for years. Even though only few organizations realized the significant value potentials described by contemporary literature sources, it is widely acknowledged that data assets can provide significant competitive benefits. Given the promises regarding value increases and competitiveness, practitioners as well as academia desire systematic approaches to transform the data sets into measurable assets. This dissertation investigates the current state of literature, conducts an empirical investigation through a structural equation modeling and applies existing theory to develop a model that allows organizations to apply a systematic approach to measure the value of Big Data specifically to their organization. With Business Process Management as the foundation of the model, IT as well as business functions will be able to successfully apply the model. Based on the assumption that Data is acknowledged as a production factor, the developed model supports organizations to justify Big Data investment decisions and thereby to contribute to competitiveness and company value. Furthermore, the findings and the model equip future researchers with a framework that can be adapted for industry-specific purposes, validated in different organizational contexts or dismantled to investigate specific success factors.

Bakgrund: Den teknologiska utvecklingen går väldigt snabbt och framväxande teknologier utgör en stor påverkan på revisionsbranschen och revisorers arbete. Enklare arbetsuppgifter automatiseras och revisorns arbete innefattas allt mer av analyser och bedömning av revisionsbevis. Det kan innebära att revisorns involvering i revisionsprocessen minskar och att revisorsyrket riskerar att förändras i grunden eller till och med försvinna till följd av automatisering. Samtidigt medför teknologin att revisorn kan spendera mer tid på värdeskapande arbetsuppgifter som kan förbättra revisionskvaliteten och öka revisorns komfort. Förändringarna som teknologin medför ställer krav på att revisorn anpassar sitt arbete till att använda teknologi, samt ställer krav på revisionsbranschen, däribland standardsättare och revisionsbyråer, att anpassa standarder, regler och riktlinjer för att stödja revisorer i deras användning av teknologi.  Syfte: Syftet med studien är att kartlägga i vilken utsträckning framväxande teknologier används av revisorer och utforska hur revisorer upplever att revisionsprocessen och deras komfort påverkas av sådana teknologier. Metod: Studien är kvantitativ och har en deduktiv ansats med tvärsnittsdesign. Använd primärdata utgörs av enkätsvar från revisionsmedarbetare i Sverige.  Resultat: Resultatet indikerar att revisionsmedarbetare, vid användning av framväxande teknologier, upplever att revisionsprocessen förbättras och att deras komfort ökar. Därtill tyder resultatet på att revisionsmedarbetare upplever att det finns ytterligare faktorer som påverkar revisionsprocessen och komforten, såsom revisionsbyråns riktlinjer för användning av teknologi och revisionsmedarbetarens kunskaper inom teknologi. Resultatet indikerar även att revisionsmedarbetares användning av teknologi generellt är låg och någon skillnad mellan större och mindre revisionsbyråer har inte identifierats. Kunskapsbidrag: Studien bidrar till litteraturen genom att fokusera på revisorns perspektiv på framväxande teknologiers påverkan på revision. Kunskap om hur revisionsprocessen och revisorns komfort påverkas bidrar till att revisionsbranschen generellt och revisionsbyråer specifikt, kan hantera utmaningarna och tillvarata möjligheterna som framväxande teknologier medför, inte minst för att stödja och vägleda revisionsmedarbetare i dess användning av teknologin. / Background: The technological development is very rapid and emerging technologies have a major impact on the auditing industry and the auditor’s work. Simpler tasks are being automated and the auditor's work is increasingly consisting of analyzes and assessment of audit evidence. This may indicate that the auditor's involvement in the audit process decreases and that the audit profession risks being replaced as a result of automation. At the same time, the technology means that the auditor can spend more time on value-creating tasks that can improve the quality of the audit and increase the auditor's comfort. The changes that technology entails mean that the auditor needs to adapt his/her work to using technology, and require that the auditing industry, including standard setters and audit firms, adapts standards, rules and guidelines to support auditors in their use of technology. Purpose: The aim of the study is to map the extent to which emerging technologies are used by auditors and to explore how auditors perceive that the audit process and their comfort are affected by such technologies. Method: The study is quantitative and has a deductive approach with cross-sectional design. Primary data is based on survey responses collected from audit staff in Sweden.  Results: The results indicate that audit staff, when using emerging technologies, experience that the audit process is improved and that their comfort increases. In addition, the results indicate that audit staff perceive that there are additional factors that affect the audit process and their comfort; the audit firm’s guidelines for the use of technology and the audit staff’s knowledge of technology. The results also indicate that auditors’ use of technology is generally low and that it does not differ between larger and smaller auditing firms. Contribution: This study contributes to the literature by focusing on the auditor's perspective on the impact of emerging technologies on auditing. Knowledge of how the auditing process and the auditor's comfort are affected contributes to the auditing industry in general and auditing firms specifically being able to manage the challenges and take advantage of the opportunities that emerging technologies bring to the industry, auditing firms and auditors.

Data quality has become an area of increasing concern in marketing research. Methods of collecting data, types of data analyzed, and data analytics techniques have changed substantially in recent years. It is important, therefore, to examine the current state of marketing research, and particularly self-administered questionnaires. This paper provides researchers important advice and rules of thumb for crafting high quality research in light of the contemporary changes occuring in modern marketing data collection practices. This is accomplished by a proposed six-step research design process that ensures data quality, and ultimately research integrity, are established and maintained throughout the research process—from the earliest conceptualization and design phases, through data collection, and ultimately the reporting of results. This paper provides a framework, which if followed, will result in reduced headaches for researchers and more robust results for decision makers.

The increasing trend in frequency of natural disasters in tandem with globalization of business makes the agricultural supply chain significantly vulnerable to disruption. This thesis presents a pragmatic approach for creating a Business Continuity Model that can notify supply chain planners when there is an increase in risk of agriculture supply chain disruption due to natural disasters. The methodology presented in this thesis applied big data analytics and machine learning algorithms along with agriculture product related exponential decay function to create a regionalized composite risk score, that incorporated both direct and indirect risk associated with the Agriculture Fresh Supply Chain. This model will aid supply chain planners in creating and implementing contingency plans, at the right time per given food production location. This risk score can help food manufacturing organizations to have a Business Continuity Plan that alleviate agriculture business supply chain interruptions. An example application of this model is illustrated with a melon packaging industry.