Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application / Datengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage. Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application

Despite the implementation of various strategies to improve outcomes, the pass rates for the National Council Licensure Exam for Registered Nurses (NCLEX-RN) for an associate degree nursing (ADN) program continue to decrease. This study examined the use of a supplemental computer-assisted formative assessment (SCAFA) as a strategy for NCLEX-RN success. A qualitative case study with a theoretical framework based on constructivism was designed to investigate nurse educators' perspectives of this particular strategy for successful outcomes. To explore these perspectives, data were collected from face-to-face interviews with nurse educators and from program documents from 1 ADN program in the southeastern United States. Guiding research questions explored nurse educators' perceptions of SCAFA and determined if and how data from these assessments were utilized. The data were analyzed using lean coding to determine emerging themes. The findings showed that a lack of consistency in the use of this tool diminishes the effectiveness of this supplemental strategy. Additional themes that emerged: educator and student attitudes, orientation and SCAFA process, resource allocation, training and preparation, and data-driven decision making. These findings were used to design a professional development project focused on the effective use of SCAFA throughout the nursing program. The study and project are expected to promote positive social change by contributing to the body of evidence on computer-assisted formative assessment, bolstering student and nurse educator learning, increasing the number of nursing students who are prepared to successfully pass the NCLEX-RN, improving program outcomes, and contributing to the professional nursing workforce.

This qualitative case study addressed the persistent achievement gaps in annual measurable objectives (AMO) data at a public rural elementary school in the Mideast United States. Response to intervention (RTI) data teams from 2010 did not produce expected student gains after 5 years of implementation in the school under study. Based on Mandinach and Jackson's data-driven decision making conceptual framework, the purpose of this study was to examine the work of the RTI data teams as they attempted to improve student learning and close achievement gaps. A purposeful sample of 13 staff members involved in the RTI implementation process was interviewed. In addition, the RTI data team and student documentation were content analyzed for process and outcomes. Open coping and thematic data analysis of the interview transcripts revealed themes of fidelity, consistency, professional development, and data use in isolation. Findings suggested that the RTI teams lack sufficient time, professional development, and the capacity to address student learning gaps adequately. As an outcome, a guiding model for designing, implementing, and evaluating ongoing blended professional development was proposed. The intent of the project is to eliminate implementation barriers and establish effective data-driven decision making practices that improve instructional practice and student learning. This study has could assist educators in their efforts to implement RTI and build organizational capacity for data-driven decision making to address persistent achievement gaps effectively.

Affärsanalys är en ökande trend som många organisationer idag använder på grund av potentialen att fastställa värdefulla insikter, ökad lönsamhet och förbättrad operativ effektivitet. Något som visat sig vara problematiskt då det önskade resultatet inte alltid är en självklarhet. Syftet med studien är att undersöka hur modeföretag kan använda datadriven affärsanalys för att generera positiva insikter genom värdeskapande mekanismer. Utifrån semistrukturerade intervjuer med anställda på ett modeföretag har vi, med utgångspunkt i tidigare forskning, kartlagt hur datadriven affärsanalys brukas för att skapa värde genom att applicera en processmodell på verksamheten. Empirin resulterade i tre värdefulla insikter (1) Det studerade företaget använder affärsanalys för ökad lönsamhet (2) Företagets data tillgångar är tillräckliga för att utvinna värdefulla insikter (3) Vidare såg vi att företaget arbetar med influencers vilket är en ny affärsanalys-funktion som inte definierats i tidigare forskning. / Business analysis is an increasingly popular trend that many organisations use because of its potential to establish valuable insights, increased profitability and improved operational efficiency. Something that has proved to be rather problematic as the desired results rarely is a certainty. The purpose of the study is to examine how fashion retailers can use business analytics to generate positive insights through value-creating mechanisms by applying a process model. Based on semi-structured interviews with the employees of a fashion company and a starting point in previous research, we have mapped how business analysis can be used to obtain value. The empirical study resulted in three valuable insights (1) The examined organisation uses business analysis to increase profitability. (2) The data assets of the organisation are enough to acquire valuable insights. (3) Further we discovered that the organisation uses influencers as a valuable asset and can be categorised as a business analysis capability, previously undefined in preceding research.

Risks are something that occur daily for all individuals and can arise from a variety of activities. If we don't take these risks into account, we will likely eventually be affected by them. By identifying and analyzing risks, the most appropriate decision can be made to mitigate or avoid the risk completely. The purpose of this study is to investigate which factors should be included in a risk analysis, and how a risk analysis can be designed for contract prioritization.  The work is carried out using an abductive scientific approach as the basis of the study. Data collection is done qualitatively through interviews. In order to achieve good scientific quality with high validity and reliability, the work was done in a systematic way with good planning.  The authors conclude that the company has a correct way of defining risks but lacks a clearly structured way of identifying new potential risks, which theory shows is important for a successful risk analysis. The result describes the financial risks as the cost critical and credit risk as the most important factor. The result describes that the company doesn't have a specific model, and the authors recommend FDFMEA as a model for risk analysis in contract prioritization, which is a developed and customized version of the traditional FMEA. The case company has a well functioning approach to how risks are handled, for example using insurance to avoid specific risks. With regards to data driven decisions, theory underlines the importance of the model presenting data in a simple and transparent way. This is something that the case company currently lacks but plans to improve. / Risker är något som förekommer dagligen för alla människor och kan uppstå ur en mängdaktiviteter, tar vi inte hänsyn till dessa risker bör vi sannolikt förr eller senare drabbas avrisken. Genom att identifiera och analysera riskerna kan det mest lämpliga beslutet fattasför att mildra eller undvika risken. Målet med denna studie är att undersöka vilka faktorersom bör ingå i en riskanalys samt hur ett riskarbete kan genomföras i en beslutsprocessför säljkontrakt. Arbetet genomförs med ett abduktivt arbetssätt som grund för studien ochdatainsamlingen sker kvalitativt genom intervjuer. För att uppnå en god vetenskapligkvalitet med hög validitet och reliabilitet utfördes arbetet på ett systematiskt arbetssättmed god planering. Författarna drar slutsatsen att företaget har ett korrekt sätt att se på risker, men saknar etttydligt strukturerat sätt att identifiera nya potentiella risker, något som teori påvisarvikten av för en lyckad riskanalys. Resultatet beskriver de finansiella riskerna som mestkritiska och kreditrisk som den viktigaste faktorn. Vidare beskriver resultatet att företagetinte har en specifik beräkningsmodell, författarna rekommenderar FDFMEA som modellför riskanalys, en utvecklad och anpassad version av traditionell FMEA analys.Fallföretaget har ett väl fungerande arbetssätt för hur risker behandlas där man tillexempel använder försäkringar för att undvika risker. I avseende på datadrivna beslutpåvisar teori vikten av att modellen presenterar data på ett enkelt och transparent sätt,något fallföretaget brister i för tillfället men planerar förbättringar.

Business model innovation has in recent year become more important for firms looking to gain competitive advantage on dynamic markets. Additionally, incorporating data into a firm’s business model has been shown to lead to improved performance. This development has led to interest in the connection between data utilisation and business model innovation. This thesis provides an in-depth case study of a Swedish space firm active within the satellite industry. The firm operates within an increasingly dynamic market, and ongoing disruptions in the form of new market entrants and rapid technological advancements has led to a search for new business opportunities. As a result, novel ways of utilising the increased amounts of data from space are of significant importance. While the firm is still realising profits utilising their incumbent business model, the firm must simultaneously explore new business opportunities to avoid extinction. The findings show that novel data utilisation, in the form of data processing, leads to business model innovation. Furthermore, the degree of business model transformation is dependent on how many of the business model's underlying elements are affected by data utilisation. Furthermore, the study concludes that a lack of trial-and-error learning impedes radical innovation efforts and hinders the development of ambidextrous capabilities within the firm. Lastly, the study finds a novel connection between the introduction of large-scale projects and improved ambidextrous capabilities. / Innovation av affärsmodeller har under senare år blivit alltmer viktigt för företag som vill uppnå ökad konkurrenskraft på dynamiska marknader. Vidare har det visat sig att företag som använder data för att förändra sin affärsmodell når bättre resultat än sina konkurrenter. Detta har lett till ett intresse för kopplingen mellan datautnyttjande och innovation av affärsmodeller. Detta examensarbete består av en fallstudie av ett svenskt rymdföretag, som har del av sin verksamhet inom satellitbranschen. Företaget verkar på en alltmer dynamisk marknad, och pågående störningar i form av nya marknadsaktörer och tekniska framsteg har lett till att företaget nu måste söka efter nya affärsmöjligheter. Som ett resultat av detta blir nya sätt att använda de ökade mängderna data från rymden av stor betydelse. Fastän företaget fortfarande framgångsrikt nyttjar sin befintliga affärsmodell, måste företaget samtidigt undersöka nya affärsmöjligheter för att undvika att hamna efter marknadsutvecklingen. Studiens resultat visar att nya sätt att använda data, i form av databehandling, leder till innovation av företagets affärsmodell. Dessutom beror graden av innovation på hur många av affärsmodellens underliggande byggstenar som påverkas av införandet av data. Studien drar vidare slutsatsen att en brist på lärande genom ’trial-and-error’ inom företaget hindrar radikala innovationsinsatser och leder till begränsade förutsättningar för att hantera organisatorisk ambidexteritet. Slutligen finner studien att storskaliga innovationsprojekt kan förbättra förutsättningarna för organisatorisk ambidexteritet.

As a result of digitalization, previously analog systems in the manufacturing industry have become digitalized, including the decision-making processes. Companies are, therefore,becoming more dependent on data for strategic decisions. However, because of the rapid development of digitalization, companies are left blindfolded in the path towards smarter manufacturing which often leads to unsuccessful technological implementations. Therefore, the thesis will explore this problem by asking: What are the required initiatives for successfully implementing digital data-driven decision-making to improve efficiency and sustainability by Swedish manufacturing companies? To answer the research questions, an exploratory multiple case study approach was conducted, where interviews with informants from the industry as well as researchers within the context of smarter manufacturing were made. The findings were then used to derive propositions which worked as the foundation of a conceptual model which functionality would be to illuminate the results in the form of a strategy map. Findings suggest that it is not always necessary for companies to implement technologies linked to large investments to enable digital data-driven decision-making. However, for those that do, there needs to be a clear organizational plan and agenda before executing theprojects since they otherwise often lead to insufficient results. That means, the technological aspects are often not the culprit in failed digital data-driven decision-makingprojects. Additional findings suggest that there are synergies connected to digital data-driven decision-making such as data-sharing possibilities that have the potential of becoming a major aspect within the context of sustainability and efficiency. / Som ett resultat av ökad digitalisering har analoga system i tillverkningsindustrin blivit digitaliserade, vilket inkluderar beslutsfattandet. Företag har därför börjat förlita sig alltmer på data för sina strategiska beslut. Men på grund av den snabba utveckling av digitalisering har tillverkningsföretagen lämnats utan klara riktlinjer för hur de bör gå tillväga för att implementera digitalt datadrivet beslutsfattande på ett effektivt men hållbart sätt. Avhandlingen kommer därför att undersöka detta problem genom att fråga: Vilka är de initiativ som krävs för att framgångsrikt implementera digital datadrivet beslutsfattande med målet att förbättra effektiviteten och hållbarheten hos svenska tillverkningsföretag? För att svara på forskningsfrågorna användes en undersökande metod med flerafallstudier, där intervjuer gjordes med informanter från industrin såväl som forskare inom ramen för smartare tillverkning. Resultaten användes sedan för att härleda förslag som därefter användes till konstruktionen av en konceptuell model vars huvuduppgift var att illustrera resultaten i form av en strategikarta. Slutsatserna pekar på att det inte alltid är nödvändigt för företag att implementera teknik kopplad till stora investeringar för att möjliggöra digitalt datadrivet beslutsfattande. Men för de som valt att implementera sådana system behövs en tydlig organisationsplan innan projekten genomförs eftersom de annars ofta leder till ofördelaktiga resultat. Detta tyder på att de tekniska aspekterna oftast inte är vad som orsakar misslyckade datadrivna beslutsprojekt. Dessutom tyder resultaten på att det finns synergier kopplade till digitalt datadrivet beslutsfattande, till exempel möjligheter att dela data som har potential att bli en viktig aspekt inom hållbarhet och effektivitet.

<p>Water infrastructure forms a critical sector of our social system and provides goods and services for public health, the natural environment, economic safety, various businesses, and government operations. In the United States (US), drinking water is supplied nationally through one million miles of pipes, most of which were installed in the early to mid-20th century with a life span of 75 to 100 years. Along with this fact, water bills which are rising faster than inflation, result in communities grappling with aging water systems, fewer water resources, and extreme weather. The federal government’s share of capital investment for water infrastructure has fallen from 31% in 1977 to 4% in 2017. Regional and state expenditure has accounted for a much larger share as federal aid for water infrastructure capital needs has declined. This has led to water rates rising to cover the costs of replacing and upgrading water infrastructure in many communities across the country. They are struggling to meet such costs through local rates and fees.</p><p>Over the next 20 years, more than 56 million new users are expected to connect to centralized treatment systems, and $271 billion is needed to meet current and future demands. However, the investment in critical water infrastructure is currently only meeting a fraction of the funding need. In 2019, the total capital spending on water infrastructure at all levels was $48 billion, while investment needs totaled $129 billion, creating an $81 billion gap. As such, the most recent American Society of Civil Engineers’ Infrastructure Report Card assigned a D to the drinking water infrastructure and a D+ to the nation’s wastewater infrastructure. Ineffectual and wasteful investment in the water sector has caused an adverse effect on grades in the infrastructure report card for water infrastructures. Moreover, this may negatively impact water-reliant sectors and water-related infrastructures due to the economic ripple effect.</p><p>This research has developed a data-driven strategic investment decision support system to close the existing water infrastructure investment gap and reduce the vulnerability of aging water infrastructure. The first phase of this study was to determine the causes affecting the grades in the infrastructure report card for drinking water and wastewater infrastructure and contributing to any latent threats and prolonged risks. It uses data-driven approaches based on analysis of existing ineffective improvement methods and recommendations. It attempts to leverage a data-driven supervised statistical learning method to capture the complex relationships between new challenges and the growing demand for water infrastructure needs. The ultimate outcome of this phase is a research approach to minimize water and wastewater vulnerability and close the investment gap to help create a paradigm shift in the current state of practice. Furthermore, improving the resiliency of and increasing investments in the water and wastewater infrastructure will lead to a resilient, efficient, and reliable water future and protect the public health of future generations.</p><p>The second phase of this study was to predict the economic benefits of additional federal support in water infrastructure among interdependent sectors within an economic system to facilitate the federal government’s share of capital investment. It conducts ripple effects analysis, which predicts the effectiveness of water infrastructure capital investment using historical economic data. It explores how federal capital investment in water infrastructure spreads economic benefits within an interdependent system. This phase was conducted at the federal level using the interindustry-macro model that analyzes macroeconomic data, including over 400 sectors. Investments that are coordinated at the federal, state, and local level will help control and stabilize rising water rates across the US.</p><p>The third phase of this study was to conduct a cost-benefit assessment in terms of private, financial, economic, and efficiency considerations using nominal and real terms to maximize the benefit of investing in the water sector and reduce the vulnerability of water infrastructures. In order to measure the costs and benefits of a strategy to maximize the efficiency of limited budgets and resources, this phase conducts a cost-benefit analysis due to the investment costs for rehabilitating and improving water infrastructures using historical economic and financial data. The long-term financial framework, including considerations of deep uncertainties so that decision-makers can understand the benefit of investing assets for an optimal level versus the cost of doing nothing and allowing the asset to run to failure is developed using the cost-benefit assessment.</p><p>Finally, a data-driven strategic investment decision support system that helps governments make water infrastructure development plans and infrastructure investment decisions in the water sector is presented. It can help governments with designing a novel system or modifying existing ineffective assessment methods and recommendations aimed at minimizing the mismatch in the water infrastructure investment gap between current spending levels and funding needs. Furthermore, minimizing the risks of ineffectual and wasteful water sector investment through rehabilitating and improving water infrastructures in a rational manner will lead to improved grades in the infrastructure report card and the resiliency of interrelated infrastructures and sectors.</p>

Nowadays, the business world is characterized by complexity since market and customer requirements are changing rapidly. Based on this assumption, providers are facing the challenge of delivering software products in shorter terms while these products remain innovative. Agile software development has a huge impact on how software is developed worldwide and promises business value in short iterations. At the same time, requirements are the base of all software products, and consequently, Requirements Engineering (RE) plays one of the most important roles in system development. Traditional techniques referring to intended data do not cover the constantly increasing demands of RE and unintended data from digital sources has amplified the need for Data-Driven Requirements Engineering (DDRE). This study contributes to Computer and System Science by providing a process to combine DDRE and traditional RE approaches in Agile software development methodologies. In this study, the researcher is trying to provide a concrete solution to the lack of an effective process to address data-driven requirements in a Scrum environment organized by regular Sprints and the purpose of it is to suggest a new method for requirements elicitation based on digital data and combine them with traditional stakeholder-driven RE in a Scrum agile environment. The method intends to assist Agile professionals to elicit requirements from digital sources in combination with intended data derived from the stakeholders without impacting the main Agile practices. The approach to conduct this study is Design Science Research (DSR) and contains five steps: Explicate Problem, Define Requirements, Design and develop Artefact, Demonstrate Artefact, and Evaluate Artefact. Literature review has been conducted to explicate the research problem and define the requirements of the artefact. Then, a process and a collaboration board have been created based on the requirements to bring DDRE and traditional RE into the Scrum environment. The researcher performed a demonstration of two illustrative cases of the usage of the proposed artefact to three Scrum professionals and three semi-structured interviews were conducted to evaluate the artefact. After the evaluation, the researcher refined and presented the final artefact that will help the public and private organizations to reduce the costs and time plan on eliciting requirements, and to increase the customers’ satisfaction. The artefact has not been applied in a real Agile environment, but Requirement Engineers and Agile team members can build on the proposed method and bring the elicitation approach of DDRE closer to the software development process.

Gravel roads are essential for economic development as they facilitate the movement of people, transportation of goods and services, and promote cultural and social development. They typically connect sparsely populated rural areas to urban centres, providing essential access for residents and entrepreneurs. Maintaining these roads to an acceptable level of service is crucial for the efficient and safe transportation of goods and services. However, substantial maintenance investmentis required, yet resources are limited. Gravel roads are prone to dust, potholes, corrugations, rutting and loose gravel. They deteriorate faster than paved roads, and their failure development is affected by traffic action and physical, geometric and climatic factors. Thus, more condition monitoring and proper road condition assessment are necessary for dynamic maintenance planning to reach efficiency and effectiveness using objective, data-driven condition assessment methods to ensure all-year-round access. However, objective data-driven methods (DDMs) are not frequently used for gravel road condition assessment, and where they have been applied, the practical implementation is limited. Instead, visual windshield assessment and manual methods are predominant. Visual assessments are unreliable and susceptible to human judgement errors, while manual methods are time-consuming and labour-intensive. Maintenance activities are predetermined despite dynamic maintenance needs, and the planning is based on historical failure data rather than the actual road condition. This thesis establishes a data-driven approach to gravel road maintenance describing the systematic assessment of the gravel road condition and collection of the condition data to ensure efficient and effective maintenance planning. This thesis uses a design research methodology based on a literature review, concept development, interview study and field experiments. A holistic approach is proposed for data-driven maintenance of gravel roads encompassing objective condition data collection, processing, analysing, and interpreting the findings for obtaining reliable information concerning the condition to gravel road decision support by utilising the opportunities presented by technological advancements, particularly sensor technology. Then, decision-making is primarily influenced by the objectively collected gravel road condition data rather than the evaluator’s perception or experience. The successful implementation of a data-driven approach depends on the quality of the collected data; therefore, data relevance and quality are emphasised in this thesis. The lack of data quality and relevance hinders effective data utilisation, leading to less precisionin decision-making and ineffective decisions. Furthermore, the thesis proposes a participatory data-driven approach for unpaved road condition monitoring, allowing road users to be part of the maintenance process and providing an efficient and effective alternative for collecting road condition data and accomplishing broad coverage at minimum cost. A top-down iiapproach for data-driven gravel road condition classification is proposed to achieve an objective assessment to address the lack of readily available quality and relevant condition data. The established data-driven approach to gravel road maintenance is evaluated and verified with field experiments on three gravel roads in Växjö municipality, Southern Sweden. The research findings indicate that properly implementing a data-driven approach to gravel road maintenance would ensure efficient and effective condition assessment and classification, which are a basis for a maintenance management system of gravel roads and enable road maintainers and authorities to achieve cost-effective decision-making. / Sustainable maintenance of gravel road