The full enforcement of the General Data Protection Regulation (GDPR) that began on May 25, 2018 forced any organization that collects and/or processes personal data from European Union citizens to comply with a series of stringent and comprehensive privacy regulations. Many software organizations struggled to comply with the entirety of the GDPR's regulations both leading up and even after the GDPR deadline. Previous studies on the subject of the GDPR have primarily focused on finding implications for users and organizations using surveys or interviews. However, there is a dearth of in-depth studies that investigate compliance practices and compliance challenges in software organizations. In particular, small and medium enterprises are often neglected in these previous studies, despite small and medium enterprises representing the majority of organizations in the EU. Furthermore, organizations that practice continuous integration have largely been ignored in studies on GDPR compliance. Using design science methodology, we conducted an in-depth study over the span of 20 months regarding GDPR compliance practices and challenges in collaboration with a small, startup organization. Our first step helped identify our collaborator's business problems. Subsequently, we iteratively developed two artifacts to address those business problems: a set of privacy requirements operationalized from GDPR principles, and an automated GDPR tool that tests these GDPR-derived privacy requirements. This design science approach resulted in five implications for research and for practice about ongoing challenges to compliance. For instance, our research reveals that GDPR regulations can be partially operationalized and tested through automated means, which is advantageous for achieving long term compliance. In contrast, more research is needed to create more efficient and effective means to disseminate and manage GDPR knowledge among software developers. / Graduate

The objective of this study is to investigate the approaches utilised by external decision-makers in their evaluation of the different facets of performance of manufacturing organisations, operating as open systems. In process, important linkages which exist among relevant performance parameters are explored. The types and frequencies of performance measures used by the sampled Portuguese financial analysts are compared using factor analysis and multiple regression analysis. Results tend to underscore the significance of the collective performance of all subsystems of the manufacturing system. Based on the findings of this research, implications focusing on the management of organisational performance systems are identified.

The motivation for the thesis was to discover whether Finnish Red Cross Youth Shelters can be evaluated and developed from legal and moral standpoints, such as the Human Rights. A special focus is put on how the international law gets interpreted and practiced in a local (Youth Shelter) context. Findings from the collected data are based on the experiences of the Youth Shleter’s employees and volunteers through workshop discussions, surveys and interviews. This observational work done within the thesis is an initial mapping of how “things are at the moment”. Data is analysed with The New Legal Realism (NLR) theory, which main focus is to study law’s context-based interpretations and mobility ie. how international law creates meanings in a local setting. (Dagan & Kreitner, 2018; 534.) The value of the findings will increase through new ideas, support measures and a better identification of the needs of employees and volunteers arising from the findings. The observational work is inspired by the idea that if someone has an obligation to ensure rights, they are also entitled to get support with knowledge of how to implement them properly.

Functional data analysis (FDA) has grown into a substantial field of statistical research, with new methodology, numerous useful applications and interesting novel theoretical developments. My dissertation focuses on the empirical properties of functional regression models and their application to financial data. We start from testing the empirical properties of forecasts with the functional autoregressive models based on simulated and real data. We define intraday returns and consider their prediction from such returns on a market index. This is an extension to intraday data of the Capital Asset Pricing model. Finally we investigate multifactor functional models and assess their suitability for the prediction of intraday returns for various financial assets, including stock and commodity futures.

Magister Commercii - MCom / Over the 1ast eight years up to 1989, a new business phenomenon has manifested itself in South Africa. During this period, the so-ca 11ed "factory shop" concept has developed into one of the fastest growing industries in both the manufacturing and commercial sectors. In South Africa, factory shops have initially established in the Cape Peninsula.4,5. The nature of these shops is such that their creation may be attributed to the clothing industry itself ;" This trend originated in about 1980 at a few clothing manufacturers in Cape Town, inter alia, Desiree, Readywear, Atlantic Knitting Mills. Originally these factory shops were open for limited hours, I usually over lunch-time and on only one Saturday per month. They were strictly limited to and for the sole benefit of I the staff of those firms. Gradually, however, family and later friends and acquaintances of these staff members became aware of the so-called bargain prices at these stores. Both supply and demand played important roles in the development of factory shops. For example, increasing pressure on management to supply larger quantities of these merchandise items at bargain prices, as well as the fact that clothing factories had excess stock which was difficult to clear through the normal distribution channels. Factory owners recognized and seized this opportunity to reduce stock at little additional cost. From their inception, factory shops have had a significant influence on the turnover and profitability of the retail clothing industry. This trend is documented in reports by the organ i zed trade, 7 ,B where reference has been made to the influence of these factory shops and where independent clothing retailers have been calling for a plan of action for protect i on against the encroachment by these factory shops on their (the clothing retailers') markets. However, up to that stage, no in-depth study or formal research was done to justify such action for protection. The ref 0reitwa s felt that" An empirical stud y of the development of factory shops in the clothing industry in the Cape Peninsula" was needed to acquire an objective view of the state of affairs.

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Agile methods have been widely implemented in the industry while teaching these methods is a substantial part of Computer Science and Engineering program curricula. Evidencedriven data of agile practices, methods, and tools have been extracted based on empirical studies with students, however, there is an important need for more anecdotal evidence to confirm these findings. In order to fill this gap, this paper explores the perceptions and the applicability of the Scrum Framework in a student research team in an industrial context. Empirical data have been gathered through interviews with the case study participants combined with a survey. The analysis reveals that student experiences are mainly positive and that they can easily grasp the benefits of Scrum Framework. The challenges of implementing Scrum in such a setting mainly concern the balance between coach and self-organization, formulating accurate user stories when researching, finding the most suitable estimation method, as well as planning when conducting research. The empirical findings may potentially be extrapolated in student scrum teams or even in industrial settings. Finally, this research paper should encourage other researchers to investigate the adoption of Scrum in a student setting. / Agila metoder har implementerats i stor utsträckning inom branschen, medan undervisning i dessa metoder är en väsentlig del av utbildningarna för datavetenskap och teknik. Bevisdriven data om agila arbetssätt, metoder och verktyg har tagits fram från empiriska studier med studenter, men det finns ett viktigt behov av mer anekdotiska bevis för att bekräfta dessa resultat. För att fylla detta gap utforskar den här rapporten uppfattningarna och tillämpligheten av ramverket Scrum i ett studentforskningsteam i ett industriellt sammanhang. Empiriska data har samlats genom intervjuer med deltagarna i fallstudien i kombination med ett frågeformulär. Analysen visar att studentupplevelserna är främst positiva och att man lätt kan förstå fördelarna med Scrum. Utmaningarna med att implementera Scrum i en sådan miljö gäller främst balansen mellan coaching och självorganiserande, formulera exakta användarhistorier när man forskar, finner den mest lämpliga uppskattningsmetoden samt planerar forskningsrelaterat arbete. De empiriska fynden kan eventuellt extrapoleras i Scrumteam med studenter eller till och med i industriella miljöer. Slutligen bör detta forskningsarbete uppmuntra andra forskare att undersöka införandet av Scrum i ett studentkontext.

In traditional meta-analysis, a random-effects model is used to deal with heterogeneity and the random-effect is assumed to be normally distributed. However, this can be problematic in the presence of outliers. One solution involves using a heavy tailed distribution for the random-effect to more adequately model the excess variation due to the outliers. Failure to consider an alternative approach to the standard in the presence of unusual or outlying points can lead to inaccurate inference. A heavy tailed distribution is favoured because it has the ability to down-weight outlying studies appropriately, therefore the removal of a study does not need to be considered. In this thesis, the performance of the t-distribution and a finite mixture model are assessed as alternatives to the normal distribution through a comprehensive simulation study. The parameters varied are the average mean of the non-outlier studies, the number of studies, the proportion of outliers, the heterogeneity and the outlier shift distance from the average mean. The performance of the distributions is measured using bias, mean squared error, coverage probability, coverage width, Type I error and power. The methods are also compared through an empirical study of meta-analyses from The Cochrane Library (2008). The simulation showed that the performance of the alternative distributions is better than the normal distribution for a number of scenarios, particularly for extreme outliers and high heterogeneity. Generally, the mixture model performed quite well. The empirical study reveals that both alternative distributions are able to reduce the influence of the outlying studies on the overall mean estimate and thus produce more conservative p-values than the normal distribution. It is recommended that a practitioner consider the use of an alternative random-effects distribution in the presence of outliers because they are more likely to provide robust results. / Thesis / Master of Science (MSc)

In the modern computing landscape, the challenge of tuning software systems is exacerbated by the necessity to accommodate multiple divergent execution environments and stakeholders. Achieving optimal performance requires a different configuration for every combination of hardware setups and business requirements. In addition, the state of the art in system tuning can involve complex statistical models and tools which require deep expertise not commonly possessed by the average software engineer. As an alternative approach to performance tuning, this thesis puts forward the use of sonification-conveying information via non-speech audio-to aid software engineers in tuning complex systems. In particular, this thesis designs, develops, and evaluates a tuning system that interactively (i.e., in response to user actions) sonifies the performance metrics of a computer system. This thesis demonstrates that interactive sonification can effectively guide software engineers through performance tuning of a computer system. To that end, a scientific survey determined which sound characteristics (e.g., loudness, panning, pitch, tempo, etc.) are best suited to express information to the engineer. These characteristics were used to create a proof-of-concept tuning system that was applied to tune the parameters of a real world enterprise application server. Equipped with the tuning system, engineers-not experts in enterprise computing nor performance tuning-were able to tune the server, so that its resulting performance surpasses that exhibited under the standard configuration. The results indicate that sound-based tuning approaches can provide valuable solutions to the challenges of configuring complex computer systems. / Master of Science

This dissertation consists of three papers related to firms' resiliency to supply chain disruptions. The first paper seeks to evaluate the effects of supply chain disruptions on firms' performance by using a recent dataset of supply chain disruptions. To this end, we analyzed operating and stock market performances of over 300 firms that experienced a supply chain disruption during 2005 to the end of 2014. The results show that supply chain disruptions are still associated with a significant decrease in operating income, return on sales, return on assets, sales, and a negative performance in total assets. Supply chain disruptions are also associated with a significant negative abnormal stock return on the day of the supply chain disruption announcements. These results are in line with previous findings in the literature. In the second paper, in order to provide a more detailed characterization of negative impacts of disruptions on firms' performance, we develop three complementary measures of system loss: the initial loss due to the disruption, the maximum loss, and the total loss over time. Then, we utilize the contingent resource-based view to evaluate the moderating effects of operational slack and operational scope on the relationship between the severity of supply chain disruptions and the three complementary measures of system loss. We find that maintaining certain aspects of operational slack and broadening business scope can affect these different measures of loss in different ways, although these effects are contingent on the disruptions' severity. The third paper examines relationships between the origin of supply chain disruptions, firms' past experience, and the negative impacts of supply chain disruptions on firms' performance. This third study shows that the impact of external and internal supply chain disruptions on firms' performance can be different when firms do and do not have past experience with similar events. For example, the results show that past experience significantly decreases initial loss, recovery time, and total loss over time experienced by firms after internal disruptions, although past experience may not decrease initial loss, recovery time, and total loss over time in the case of external disruptions. / Ph. D. / Supply chain disruptions occur frequently in today’s complex and interdependent business environment. The Kumamoto earthquakes, Hanjin Shipping’s bankruptcy, and Hurricanes Harvey and Irma, are just a few events that led to major supply chain disruptions in the U.S. and other parts of the world in 2016 and 2017 alone. In this dissertation, we first use a recent dataset of supply chain disruptions to evaluate the effects of supply chain disruptions on firms' performance. The results show that supply chain disruptions are still associated with significant negative impacts on firms’ performance as they have been shown to be in previous studies of earlier datasets. Next, we provide a broader assessment of supply chain disruptions’ impacts on firms’ performance. To accomplish this, we specifically consider the negative impacts with respect to three complementary metrics borrowed from the systems resilience literature: the initial loss, the maximum loss, and the total loss over time. The initial loss and maximum loss metrics evaluate different characteristics of the magnitude of a disruption’s impact on a firm’s performance, whereas total loss over time gives a broader measure of the overall effect of that disruption on that firm, over time. By adopting a more comprehensive view of firms’ performance through the use of such systems resilience concepts, we develop new and expanded inferences about how and when maintaining operational slack and broadening operational scope can benefit firms by helping to reduce the negative impacts of disruptions. Finally, we study the relationships between the negative impacts of supply chain disruptions on firms’ performance, the origin of supply chain disruptions, and firms’ prior experience. The results show that the impact of internal and external supply chain disruptions on firms’ performance can be different when firms do and do not have past experience with similar events. In particular, the results show that past experience significantly decreases initial loss, recovery time, and total loss over time experienced by firms after internal disruptions. However, past experience may not decrease initial loss, recovery time, and total loss over time in the case of external disruptions.