Background and Motivation. Requirements engineering is decision intensiveand involves many roles and stakeholders. As humans are often subjective in theirdecision-making and biased by subjective criteria, we are interested in exploring howthis impacts requirements prioritization. Each requirements prioritization techniquehas its advantages and limitations to use on software products for single/multiplepurposes in the software field. Understanding how human aspects affect requirementsprioritization remains greatly unexplored. Objectives. This thesis aims to understand how human factors impact requirementsprioritization. The primary goal is to address and understand the various human as-pects that affect people when they make decisions. The secondary goal is to identifyvarious human aspects that receive more attention while prioritizing requirements. Methods. Systematic Literature Review (SLR) and survey were chosen as the re-search methods for this thesis. A snowballing method was used to extract empiricalresearch papers that were used for implementing the survey questionnaire. Each em-pirical paper from snowballing method has identified some human aspects throughone or more prioritization techniques and prioritization criteria. Using these humanaspects as input a survey questionnaire is designed for gaining insights on occur-rences/experiences of these human aspects in a large organization of Agile practi-tioners. Results. From the literature review, we identified 21 papers through the snow-balling method. And we identified more than two human aspects from each SLRpaper that impact requirements prioritization that were grouped into 11 categories.We also discovered many requirements prioritization techniques and their criteriawhere we included the top 15 RP techniques, 11 human aspects, and 17 RP cri-teria in the web-based survey questionnaire that were extracted through the SLRapproach. Our survey respondents considered the human aspects as very importantare Domain Knowledge of Individuals/ Stakeholders/ Analysts; Ability to consid-er/understand multiple perspectives; Ability to build/reach Consensus; Cognitiveskills and Limitations; Group Cohesion/ Team Maturity; and Accept Diversity as-pects as having the largest impact when prioritizing requirements. We have alsodiscovered that Emotions/ Emotional Cohesion which is also rated by the surveyrespondents as very important and is having the least impact as a human aspectwhen prioritizing requirements. Conclusions. Our study focus on the human aspects in requirements prioritizationmethod, the actual human aspects are least graded and human behavior that is con-sidered as an human aspect is highly graded by the practitioners in the survey. So aclear map is needed to identify the human aspect bias for requirements prioritizationand the results of this study can be helpful to all the researchers who want to carryour research on requirements prioritization in relation with human aspects.

Bakgrund: Allt fler ungdomar har en försämrad sömn. Sömn är en viktig faktor för välmående och hälsa. Ungdomar har större behov av sömn än barn och vuxna, men eftersom ungdomar ofta har stora variationer i sitt sömnmönster kan det leda till att rekommenderade sömntimmar inte uppnås. Det finns olika orsaker som påverkar ungdomars sömn.  Syfte: Att beskriva hur försämrad sömnkvalitet påverkar ungdomars hälsa. Metod: En systematisk integrativ litteraturstudie med induktiv ansats. Datainsamlingen genomfördes i databaserna Cinahl, Medline, Pubmed och Scopus. Totalt inkluderades och analyserades 14 vetenskapliga artiklar. Resultatet: Resultatet visar att försämrad sömnkvalitet hos ungdomar är en bidragande orsak till ohälsa och bristande utbildning. Ur analysen framkom två kategorier; Påverkan på välbefinnandet och Påverkan på utbildning. I kategorin Påverkan på välbefinnandet framkom fyra underkategorier: Destruktivt beteende, Trötthet, Depression och Försvårade sociala relationer. I kategorin Påverkan på utbildning framkom två underkategorier: Försvagade skolprestationer och Skapar en förskjuten dygnsrytm. Konklusion: Bristande sömnkvalitet påverkar ungdomar fysiskt, psykiskt och socialt. Det kan leda till en försämrad hälsa och skolgång. Skolsköterskan behöver ha förståelse och kunskap om ungdomars försämrade sömnkvalitet och dess effekter i det hälsofrämjande arbetet. / Background: More and more young people have impaired sleep. Sleep is an important factor for well-being and health. Young people have a greater need for sleep than children and adults, but because young people often have large variations in their sleep patterns, this can lead to the recommended hours of sleep not being achieved. There are various reasons that affect young people's sleep. Purpose: To describe how poor sleep quality affects adolescents' health. Method: An integrative literature review with an inductive approach. The data collection was carried out in the databases Cinahl, Medline, Pubmed and Scopus. A total of 14 scientific articles were included and analyzed. The result: The result shows that a poor sleep quality in adolescents´ is a contributing cause to ill health and lack of education. Two categories emerged from the analysis; Impact on well-being and Impact on education. In the category Impact on well-being, four subcategories emerged: Destructive behavior, Fatigue, Depression and Impaired social relationships. In the category Impact on education, two subcategories emerged: Weakened school performance and Craetes a shifted circadian rhythm.  Conclusion: Lack of sleep quality affects adolescents´ physically, psychologically and socially. It can lead to a deterioration in health and schooling. The school nurse needs to have understanding and knowledge of the deteriorating quality of young people and its effects in the health promotion work.

Youths tend to spend less and less time reading, although they still consider that they would like to read more. This dilemma is particularly interesting within the teaching of the Swedish language in school since reading fiction is a vital part of the subjects’ content. The aim of this systematic literature study is to investigate upper secondary students’ perceptions of fiction reading. The current method implies searching for literature within a certain subject, followed by a critical review and coding of the chosen literature. The time range that is being investigated is from the 1970s and onwards, that is, a Swedish subject determined by three separate curriculums. The study shows that students tend to have a different amount of interest in reading but an important part of reading seems to be the possibility to choose the book yourself. The study can also demonstrate a distinct difference between the reading that students do at home and the reading that takes place in school.

A continuous improvement project (CIP) is a structured improvement project using a team of people "typically representing different departments or units in the organization" working to improve a process or work area over a relatively short period of time, such as a few days or up to several months. A CIP may use different improvement methodologies and tools, and may thus be defined according to the improvement approach. For instance, an organization adopting Lean as an improvement approach is likely to have CIPs implementing Lean tools, such as 5S or value stream mapping. These projects may be referred to as Lean projects in general, although they may also represent accelerated improvement projects such as Kaizen events, Kaizen blitz, or rapid improvement projects. Alternatively, an organization utilizing Six Sigma as an improvement approach may have Six Sigma projects that use the Define-Measure-Analyze-Improve-Control (DMAIC) process and statistical tools. Some organizations adopt an integrated improvement approach, such as Lean Six Sigma, and therefore may have CIPs with an even broader set of tools from which to choose. Lastly, many organizations may have an improvement approach not characterized by any single set of improvement processes and tools, and thus, may be thought of generally as process improvement, or quality improvement, projects using a traditional methodology as plan-do-study/check-act (PDSA or PDCA). In this dissertation, all of these types of improvement projects are referred as CIPs. Since the 1980s, hospitals have been using CIPs to address some of the problems in hospitals, such as quality in healthcare delivery, internal process efficiency, communication and coordination, and the cost of services. Some hospitals have achieved significant improvements, such as reducing the turnaround time for clinical laboratory results by 60 percent and reducing instrumentation decontaminations and sterilization cycle time by 70 percent. However, as with many other companies, hospitals often experience difficulty achieving their desired level of improvements with CIPs. Therefore, the purpose of this dissertation is to identify the critical success factors (CSFs) related to CIP success. In order to achieve this goal, five objectives were achieved: creating a methodology to assess the maturity or evolution of a research field (manuscript #1), identifying a comprehensive list of CSFs for CIPs (manuscript #2), assessing the maturity of the published literature on CIPs in hospitals (manuscript #3), identifying the most important factors related to CIPs in hospitals (manuscript #4) , and conducting an empirical investigation to define the CSFs for CIPs in hospital settings (manuscript #5 and #6). This investigation was conducted in three phases: research framing, variable reduction, and model development and testing. During these phases, the researcher used the following methodologies and data collection tools: systematic literature review, maturity framework (developed as part of this dissertation), expert study, retrospective survey questionnaire, exploratory factor analysis, partial-least squares structural equation modeling, and regression modeling. A maturity framework with nine dimensions was created (manuscript #1) and applied in order to identify a list of 53 factors related to CIP in general, involving any organization (manuscript #2). Additionally, the maturity framework was used to assess the literature available on CIPs in hospitals, considering only the authorship characteristic dimension (manuscript #3). Considering the frequency of new authors per year, the relative new integration of research groups, and the limited set of predominant authors, the research field, or area, of CIPs in hospitals is one with opportunities for improving maturity. Using the systematic literature review from manuscript #3, the list of 53 factors, and the list of predominant authors, a review of the literature was conducted, along with an expert study to more fully characterize the importance of various factors (manuscript #4). A conclusion from this particular work was that it is not possible to reduce the list of 53 factors based on these results, thus, a field study using the complete comprehensive list of factors was determined to have stronger practical implications. A field study was conducted to identify factors most related to CIP perceived success (manuscript #5) and CIP goal achievement (manuscript #6). The final results and practical implications of this dissertation consist in the identification of the following CSFs for CIP success in hospitals: Goal Characteristics, Organizational Processes, Improvement Processes, and Team Operation. These CSFs include several specific factors that, to the researcher's knowledge, have not been previously studied in empirical investigations: goal development process, organizational policies and procedures, CIP progress reporting, and CIP technical documentation. Practitioners involved with CIPs, such as CIP leaders, facilitators, stakeholders/customers, and continuous improvement managers/leaders, can utilize these results to increase the likelihood of success by considering these factors in planning and conducting CIPs. / Ph. D.

Yes / This paper aims to focus on systematically analysing and synthesising the extant research published on supply chain integration (SCI) area, given the significance of SCI research area. More specifically, the authors aim to answer three questions: “Q1 – What are the factors (e.g. both driving and inhibiting) that influence SCI?”, “Q2 – What are the key developments (e.g. both in research and industry) in SCI area?” and “Q3 – What are the approaches employed/discussed to integrate supply chains?”. Over the past decade, SCI has gained increasing attention in the supply chain management (SCM) context, both from the practitioners’ perspective and as a research area. In realising the global transformations and competitive business environment, a number of organisations are collaborating with their supply chain (SC) partners, to conduct seamless SC operations. A systematic and structured literature review is carried out to observe and understand the past trends and extant patterns/themes in the SCI research area, evaluate contributions and summarise knowledge, thereby identifying limitations, implications and potential directions of further research. Thus, to trace the implementation of SCI practices, a profiling approach is used to analyse 293 articles (published in English-speaking peer-reviewed journals between 2000 and 2013) extracted from the Scopus database. The Systematic Review Approach proposed by Tranfield et al. (2003) was followed to analyse and synthesise the extant literature on SCI area. The analysis presented in this paper has identified relevant SCI research studies that have contributed to the development and accumulation of intellectual wealth to the SCI and SCM area. Each of the 293 papers was examined for achieving the aim and objectives of the research, the method of data collection, the data analysis method and quality measures. While some of the papers provided information on all of these categories, most of them failed to provide all the information, especially for Q2 and Q3 that resulted in 23 and 21 papers, respectively. This study would have benefited from the analysis of further journals; however, the analysis of 293 articles from leading journals in the field of operations and SCM was deemed sufficient in scope. Moreover, this research has implications for researchers, journal editors, practitioners, universities and research institutions. It is likely to form the basis and motivation for profiling other database resources and specific operations and SCM-type journals in this area. This systematic literature review highlights a taxonomy of contextual factors driving and inhibiting SCI for researchers and SC practitioners to refer to while researching or implementing SCI. It also exemplifies some areas for future research, along with the need for researchers to focus on developing more practical techniques for implementing SCI and improving organisational performance. The prime value and uniqueness of this paper lies in analysing and compiling the existing published material in relation to Q1, Q2 and Q3, including examining other variables (such as yearly publications, geographic location of each publication, type of publication, type of research methods used), which lacks in the recent published five SCI literature review-based articles (by Kim, 2013; Leuschner et al., 2013; Alfalla-Luque et al., 2013; Parente et al., 2008; Fabbe-Costes and Jahre, 2007). This has been achieved by extracting and synthesising existing publications using “Supply Chain Integration” keyword. This paper provides a critique of the conceptual and empirical works in SCI discipline and offers research agendas that can stimulate future researchers to carefully explore the topic.

Yes / The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.

Yes / The use of Artificial Intelligence (AI) in Digital technologies (DT) is proliferating a profound socio-technical transformation. Governments and AI scholarship have endorsed key AI principles but lack direction at the implementation level. Through a systematic literature review of 59 papers, this paper contributes to the critical debate on the ethical use of AI in DTs beyond high-level AI principles. To our knowledge, this is the first paper that identifies 14 digital ethics implications for the use of AI in seven DT archetypes using a novel ontological framework (physical, cognitive, information, and governance). The paper presents key findings of the review and a conceptual model with twelve propositions highlighting the impact of digital ethics implications on societal impact, as moderated by DT archetypes and mediated by organisational impact. The implications of intelligibility, accountability, fairness, and autonomy (under the cognitive domain), and privacy (under the information domain) are the most widely discussed in our sample. Furthermore, ethical implications related to the governance domain are shown to be generally applicable for most DT archetypes. Implications under the physical domain are less prominent when it comes to AI diffusion with one exception (safety). The key findings and resulting conceptual model have academic and professional implications.

Yes / The unique characteristics of games have led scientific research to increasingly focus on their potential role in learning processes. Currently, their effectiveness in fostering experiential learning and skill acquisition in several areas is already supported by the existing evidence, mainly about the potential of digital games. Paradoxically, the current post-digital era seems to have led to a growing popularity of analogue games. The present Systematic Literature Review aimed to map the existing literature on the potential of board, tabletop, or other analogue games in learning processes. It intended to systematize the contemporary state of the art (2012-2022) around the pedagogical role of these games, their effectiveness, the promoted learning outcomes, the methodological aspects of the interventions, the used games – including mechanics and other characteristics – and the current discussions around inclusion and accessibility in analogue game-based learning. Adopting the PRISMA methodology, we searched ACM Digital Library, EBSCO, ERIC, Scopus - Elsevier, and Web of Science databases, as well as other peer-reviewed “grey literature” sources. The search resulted in an initial sample of 2741 articles that was then screened by inclusion and exclusion criteria previously defined according to the research objectives. We obtained a final sample of 45 articles. To formulate the mapping of existing research, these studies were analyzed using a combination of statistical, content, and critical analysis procedures. The obtained results support the role of board, tabletop, and other analogue games in educational contexts – based on their educational potential – with a broad range of knowledge, cognitive, and psychological outcomes. The study also emphasized the relevance of these games in the promotion of soft skills and other aspects typically associated with meaningful learning, such as engagement, satisfaction, flexibility, and freedom of experimentation. However, important limitations were found in a fair amount of the pedagogical approaches studied, which can be mostly attributed to the low prevalence of modern board games that relate what is intended to be learned to aspects of game design and have little to no consideration of accessibility and inclusion aspects in these studies. / The present work was developed in the scope of the Project Training the Educators to Facilitate the Teaching and Assessment of Abstract Syllabus by the Use of Serious Games – TEGA (2020-1- UK01-KA203-079248), funded by the European Commission on the scope of Erasmus+ Programme. The research team also acknowledges the funding by Fundação para a Ciência e para a Tecnologia (FCT) provided to CICANT R&D Unit (UIDB/05260/2020), on the scope of Verão com Ciência initiative, which allowed the inclusion of a research initiation grant holder as co-author of the present work.

Academic success requires not only taking in content, but also understanding how to learn best. Self Regulated Learning (SRL) is process by which humans regulate their thinking, emotions, and behavior. It broadly describes the process of knowing (or learning) how to learn. Education research has found Self-Regulated Learning to be a key predictor of academic success along with other constructs like motivation and self-efficacy. It may be particularly critical in learning to program at the post-secondary level. Studies have shown that students benefit greatly from targeted instruction in these skills. Teaching students how to better self-regulate is both important and valuable for Computer Science students. The solution here may seem straightforward: educators should give instruction on self-regulation skills. However, there are a number of skills that encompass a student's proficiency with self-regulate; including time management, problem decomposition, and reflection. Self regulation also tends to be a highly cognitive and internal process making it difficult to observe directly, let alone measure. Which skills should be prioritized for targeted instruction? How could we empirically measure those skills? What limitations should we keep in mind when making such decisions? Within this dissertation, I will seek to address these questions. In order to get an idea of what skills the Computing Education Research community should be prioritizing, my co-authors and I conducted two studies. First, a Delphi Process study that expanded the field by gaining an understanding of what SRL skills CS post-secondary educators value most. This gave a more firm view of what skills were most important for CS students. Second, a systematic literature review to examine what skills had been studied within the Computing Education Research community. Ultimately, I created a finalized list of 12 SRL skills that appear to be particularly important to CS education. This list also includes behaviors an outside observer could use as indicators of the presence or absence of SRL. After creating this list, I then considered how best to measure these each of these 12 skills. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong inferences about a student's internal state empirically. They also allow for measurement of students at greater scale and through automated means, making them advantageous for large classes. For my third publication, I then set about identifying a set of data traces for these skills taking a theory-first approach. I also make the case that CS is well situated to make great gains in trace-based approaches as they make use of a whole ecosystem of data sources. This is important as it is currently common for studies to utilize just one. / Doctor of Philosophy / Knowing how to learn is a critical aspect to academic success. Self-Regulation is the process by which humans regulate their thinking, emotions, and behavior. It encompasses the process of knowing (or learning) how to learn. Several studies have argued that learning Computer Science especially requires a strong self-regulated learning, but studies show novice programmer's skills in this area are still weak and benefit from further instruction. This is true even for students entering post-secondary education. Thus teaching students how to better self-regulate is important for CS students, but creating such lessons is not straightforward. SRL is a broad field and covers a variety of different skills that students may need. What skills are most important for instructors to teach their students? Once we know what skills are most important for targeting, how do we measure those skills? These are the questions I examine. In order to get an idea of what skills the Computing Education Research community should be prioritizing, I conducted both a Delphi Process study. Following that I conducted a systematic literature review to get a better idea of what the Computing Education Research community is currently studying. I then considered the best way to measure these skills. While there are many approaches available to study SRL, I opted to examine these skills through student interactions with digital education software, called data traces. These traces are advantageous as they authentically capture learning in a way no other approach currently can. For my third paper I systematically derived a series of high-quality traces and made the case that CS classes already collect a lot of valuable traces through common digital education software systems.

Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put right. Defect Prediction is an important part of Software Engineering. It allows developers to prioritise the code that needs to be inspected when trying to reduce the number of defects in code. A small change in the number of defects found will have a significant impact on the cost of producing software. Aims. The aim of this dissertation is to investigate the factors which a ect the performance of defect prediction models. Identifying the causes of variation in the way that variables are computed should help to improve the precision of defect prediction models and hence improve the cost e ectiveness of defect prediction. Methods. This dissertation is by published work. The first three papers examine variation in the independent variables (code metrics) and the dependent variable (number/location of defects). The fourth and fifth papers investigate the e ect that di erent learners and datasets have on the predictive performance of defect prediction models. The final paper investigates the reported use of di erent machine learning approaches in studies published between 2000 and 2010. Results. The first and second papers show that independent variables are sensitive to the measurement protocol used, this suggests that the way data is collected a ects the performance of defect prediction. The third paper shows that dependent variable data may be untrustworthy as there is no reliable method for labelling a unit of code as defective or not. The fourth and fifth papers show that the dataset and learner used when producing defect prediction models have an e ect on the performance of the models. The final paper shows that the approaches used by researchers to build defect prediction models is variable, with good practices being ignored in many papers. Conclusions. The measurement protocols for independent and dependent variables used for defect prediction need to be clearly described so that results can be compared like with like. It is possible that the predictive results of one research group have a higher performance value than another research group because of the way that they calculated the metrics rather than the method of building the model used to predict the defect prone modules. The machine learning approaches used by researchers need to be clearly reported in order to be able to improve the quality of defect prediction studies and allow a larger corpus of reliable results to be gathered.