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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.

Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. This thesis presents the supervised learning algorithm to perform predicative data analytics in Embedded Test System at the Nordic Engineering Partner company. Predictive Maintenance is a concept that is often used in manufacturing industries which refers to predicting asset failures before they occur. The machine learning algorithms used in this thesis are support vector machines, multi-layer perceptrons, random forests, and gradient boosting. Both binary and multi-class classifier have been provided to fit the models, and cross-validation, sampling techniques, and a confusion matrix have been provided to accurately measure their performance. In addition to accuracy, recall, precision, f1, kappa, mcc, and roc auc measurements are used as well. The prediction models that are fitted achieve high accuracy.

Air quality assessment plays a crucial role in understanding the impact of air pollution onhuman health and the environment. With the increasing demand for accurate assessment andprediction of air quality, advanced data analytics modelling techniques offer promisingsolutions. This thesis focuses on leveraging advanced data analytics to assess and analyse airpollution concentration levels in Italy over a 4km resolution using the FORAIR_IT datasetsimulated in ENEA on the CRESCO6 infrastructure, aiming to uncover valuable insights andidentifying the most appropriate AI models for predicting air pollution levels. The datacollection, understanding, and pre-processing procedures are discussed, followed by theapplication of big data training and forecasting using Apache Spark MLlib. The research alsoencompasses different phases, including descriptive and inferential analysis to understand theair pollution concentration dataset, hypothesis testing to examine the relationship betweenvarious pollutants, machine learning prediction using several regression models and anensemble machine learning approach and time series analysis on the entire dataset as well asthree major regions in Italy (Northern Italy – Lombardy, Central Italy – Lazio and SouthernItaly – Campania). The computation time for these regression models are also evaluated and acomparative analysis is done on the results obtained. The evaluation process and theexperimental setup involve the usage of the ENEAGRID/CRESCO6 HPC Infrastructure andApache Spark. This research has provided valuable insights into understanding air pollutionpatterns and improving prediction accuracy. The findings of this study have the potential todrive positive change in environmental management and decision-making processes, ultimatelyleading to healthier and more sustainable communities. As we continue to explore the vastpossibilities offered by advanced data analytics, this research serves as a foundation for futureadvancements in air quality assessment in Italy and the models are transferable to other regionsand provinces in Italy, paving the way for a cleaner and greener future.

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<p dir="ltr">Tree-based machine learning models, such as decision trees and random forests, are one of the most widely used machine learning models primarily because of their predictive power in supervised learning tasks and ease of interpretation. Despite their popularity and power, these models have been found to produce unexpected or discriminatory behavior. Given their overwhelming success for most tasks, it is of interest to identify root causes of the unexpected and discriminatory behavior of tree-based models. However, there has not been much work on understanding and debugging tree-based classifiers in the context of fairness. We introduce FairDebugger, a system that utilizes recent advances in machine unlearning research to determine training data subsets responsible for model unfairness. Given a tree-based model learned on a training dataset, FairDebugger identifies the top-k training data subsets responsible for model unfairness, or bias, by measuring the change in model parameters when parts of the underlying training data are removed. We describe the architecture of FairDebugger and walk through real-world use cases to demonstrate how FairDebugger detects these patterns and their explanations.</p>