Individualität und ökologisches Bewusstsein gewinnen in der gesellschaftlichen Wahrnehmung immer stärker an Bedeutung. Entsprechend dieser Treiber sind Menschen zunehmend interessiert, Informationen zu erhalten, die individuell auf die eigenen Bedürfnisse angepasst und auf die aktuelle Umgebungssituation abgestimmt sind. In der heutigen Wissensgesellschaft bilden Umweltinformationen dabei ein zentrales Element zur Bewertung ökologischer und ökonomischer Systemzusammenhänge. Zur Erfassung und wissenschaftlichen Beschreibung derartiger Informationen fehlen bislang geeignete Methoden, sodass sich die Dissertation diesem Thema nähert. Die vorliegende Arbeit beschreibt die Potentiale und Herausforderungen bei der Aggregation und Inwertsetzung individueller Umweltinformationen in urbanen Ökosystemen. Ziel dabei ist die Entwicklung und Evaluierung eines serviceorientierten Frameworks unter Nutzung einer modellgetriebenen Prozessintegration. Urbane Gebiete zeichnen sich insbesondere durch heterogene Strukturen und einer hohen zeitlichen Dynamik von Umweltkenngrößen aus und bilden somit sehr komplexe Ökosysteme. In diesen Punkten unterscheiden sich urbane Ökosysteme deutlich von Ökosystemen des ländlichen Raums. Charakteristische Merkmale wie intensive Flächennutzung, dichte Bebauung aber auch starke anthropogene Einflüsse haben Auswirkungen auf physikalische, chemische und biologische Prozesskreisläufe. Dadurch bilden sich in Städten gegenüber dem ländlichen Raum Besonderheiten aus, ein sog. Mesoklimaraum. Aufbauend auf einer allgemeinen Systemanalyse wird das Konzept des \textit{Raster Model Exposure Pattern} und des \textit{Exposure Data Service} eingeführt. Diese beiden Artefakte bilden eine Transformationsstrategie um ganzheitlich umwelt- und informationswissenschaftliche Betrachtungen durchzuführen, sowie individuelle Expositionen in urbanen Ökosystemen zu aggregieren.:1. Einleitung 1.1. Motivation 1.2. Forschungsgegenstand 1.3. Forschungsziel 1.4. Erwartete Ergebnisse 1.5. Forschungsmethode 1.6. Aufbau der Arbeit 2. Inwertsetzung individueller Umweltinformationen 2.1. Ein Blick in die Stadt der Zukunft 2.2. Anwendungsszenarien für individuelle Umweltinformationen 2.2.1. Individuelle Umweltinformationen zur Entscheidungsfindung 2.2.2. Individuelle Umweltinformationen zur Minimierung von Risiken 2.3. Exposom und Exposition 3. Systembeschreibung urbaner Ökosysteme 3.1. Hintergrund 3.2. Allgemeine Systemanalyse 3.3. Modelle im Kontext urbaner Ökosysteme 3.3.1. Konzeptionelles Modell 3.3.2. Technisches Modell 3.4. In situ Daten 3.5. Transformation 3.5.1. KDD Prozess 3.5.2. Wissensbasierte Systeme 3.5.3. Regelbasis - Regelbasierte Systeme 3.5.4. Regelbasis - Maschinelles Lernen 3.6. Systemmanagement 4. Urbanes Klima - ein komplexes Phänomen 4.1. Hintergrund 4.2. Das Phänomen von Wärmeinseln in urbanen Ökosystemen 4.3. Urbanisierung 4.4. Heterogenität urbaner Strukturen 4.5. Hohe Dynamik von Zustandsgrößen 4.6. Forschungskonzept zur Bestimmung individueller Expositionen in urbanen Ökosystemen II. Konzeptionelles Vorgehensmodell zur Bestimmung individueller thermischer Expositionen in urbanen Ökosystemen 5. Raster Model Exposure Pattern 5.1. Analyse 5.2. Implementierung 5.2.1. Import der Rasterdaten und Bewegungsdaten 5.2.2. Zeitdiskretisierung der Bewegungsdaten 5.2.3. Georeferenzierung der Bewegungsdaten auf die Rasterelemente 5.2.4. Abfrage der Exposition 5.2.5. Berechnung der Individuellen Exposition 6. Exposure Data Services 6.1. Hintergrund 6.2. Vorgehensmodell 6.3. Die Transformation im Exposure Data Service 6.4. Die Durchführung der Systemanalyse im Exposure Data Service 6.5. Bestimmung der in situ Daten im Exposure Data Service 6.6. Das Systemmanagement im Exposure Data Service 7. Wärme in der Stadt - ein Fallbeispiel 7.1. Transformation 7.2. Systemanalyse 7.2.1. Definition der Zielkenngröße 7.2.2. Definition des Untersuchungsgebietes 7.2.3. A-priori Informationen über das Untersuchungsgebiet 7.2.4. Rasterung des Untersuchungsgebietes 7.2.5. Klassifizierung von Kontext und In situ Daten 7.2.6. Klassifizierung der Thermischen Exposition 7.2.7. Erarbeitung einer Monitoringstrategie 7.3. in situ Daten 7.3.1. Definition der Kenngrößen 7.3.2. Spezifikation der Datenquellen 7.3.3. Auswahl geeigneter Datenquellen 7.4. Systemmanagement 7.4.1. Entwicklung einer Systemarchitektur 7.4.2. Spezifikation der Komponenten 7.4.3. Erfassung der Datenflüsse 7.4.4. Inbetriebnahme der IT Infrastruktur 8. Thermische Charakterisierung 104 8.1. Material und Methode 8.2. Ergebnis und Auswertung 9. Instanziierung 9.1. Programmlogik 9.1.1. UI Rules Engine 9.1.2. Datenschnittstelle - API 9.1.3. Map Prozessierung 9.2. Prozessierung der Indikatoren 9.2.1. LST Prozessierung 9.2.2. NDVI Prozessierung 9.2.3. Landnutzung Prozessierung 9.2.4. Prozessierung der subjektiven Temperatur 9.2.5. Wärmeindex Prozessierung 9.3. Zusammenhängende Betrachtung der Indikatoren 10. Synthese und Diskussion 11. Schlussbetrachtung 11.1. Zusammenfassung 11.2. Forschungsbeitrag 11.3. Ausblick Literaturverzeichnis

Forest and forestland are important roles and sources of livelihood for the population living in or near forests and in mountainous areas of Vietnam. The objectives of this paper analysed the change in forest resource, and policy of forestry in Vietnam. In recent several years, forest area rapidly covered an average rate of 240,000 ha/year and had about 13.39 million hectares in 2010. It has contributed to the use of bare land, job creation and improvement of livelihoods for 25% of Vietnam’s population living in mountainous areas. Those results were the purpose of reforestation program and the production of wood industry in Vietnam. In this addition, government policies and regulations have provided a solid foundation for development of the forest plantations and conservation of forest ecosystems though forest land allocation and lease to organizations, households, and individuals. Therefore, the forest utilization has motivated by both environmental and commercial factors in Vietnam based on dividing into three forest categories special use, protection and production forests. However, the development strategy of forest management plan is the difficulties associated with conflicting land claims and boundary disputes due to the value of the established forest. / Rừng và đất rừng đóng vai trò quan trọng và là nguồn sinh kế cho người dân sống trong hoặc gần rừng ở các khu vực miền núi của Việt Nam. Mục tiêu của nghiên cứu này phân tích sự thay đổi về tài nguyên rừng và chính sách về lâm nghiệp. Trong một vài năm gần đây, diện tích rừng bao phủ nhanh với tốc độ trung bình 240.000 ha/năm và có khoảng 13,39 triệu ha trong năm 2010 này đã góp phần vào việc sử dụng đất trống, tạo việc làm và cải thiện đời sống cho 25% dân số sống ở khu vực miền núi của Việt Nam. Kết quả này là mục đích của chương trình trồng rừng và sản xuất gỗ công nghiệp tại Việt Nam. Bên cạnh đó, chính sách và các quy định của chính phủ đã cung cấp một nền tảng vững chắc cho việc phát triển diện tích trồng rừng và bảo tồn hệ sinh thái rừng mặc dù rừng và đất rừng đã được giao và khoán cho các tổ chức, hộ gia đình, cá nhân. Vì vậy, việc sử dụng rừng đã thúc đẩy bởi hai yếu tố môi trường và thương mại ở Việt Nam, dựa trên phân loại rừng: rừng đặc dụng, rừng sản xuất và rừng phòng hộ. Tuy nhiên, chiến lược kế hoạch quản lý phát triển rừng có những khó khăn liên quan đến xung đột khiếu nại đất và tranh chấp biên giới do giá trị của rừng được thành lập.

Variability modeling is one of the key disciplines to cope with complex variability in large software product lines. It aims at creating, evolving, and configuring variability models, which describe the common and variable characteristics, also known as features, of products in a product line. Since the introduction of feature models more than twenty years ago, many variability modeling languages and notations have been proposed both in academia and industry, followed by hundreds of publications on variability modeling techniques that have built upon these theoretical foundations. Surprisingly, there are relatively few empirical studies that aim at understanding the use of such languages. What variability modeling concepts are actually used in practice? Do variability models applied in real-world look similar to those published in literature? In what technical and organizational contexts are variability models applicable? We present an empirical study that addresses this research gap. Our goals are i) to verify existing theoretical research, and ii) to explore real-world variability modeling languages and models expressed in them. We study concepts and semantics of variability modeling languages conceived by practitioners, and the usage of these concepts in real, large-scale models. Our aim is to support variability modeling research by providing empirical data about the use of its core modeling concepts, by identifying and characterizing further concepts that have not been as widely addressed, and by providing realistic assumptions about scale, structure, content, and complexity of real-world variability models. We believe that our findings are of relevance to variability modeling researchers and tool designers, for example, those working on interactive product configurators or feature dependency checkers. Our extracted models provide realistic benchmarks that can be used to evaluate new techniques. Recognizing the recent trend in software engineering to open up software platforms to facilitate inter-organizational reuse of software, we extend our empirical discourse to the emerging field of software ecosystems. As natural successors of successful product lines, ecosystems manage huge variability among and within their software assets, thus, represent a highly interesting class of systems to study variability modeling concepts and mechanisms. Our studied systems comprise eleven highly configurable software systems, two ecosystems with closed platforms, and three ecosystems relying on open platforms. Some of our subjects are among the largest successful systems in existence today. Results from a survey on industrial variability modeling complement these subjects. Our overall results provide empirical evidence that the well-researched concepts of feature modeling are used in practice, but also that more advanced concepts are needed. We observe that assumptions about variability models in the literature do not hold. Our study also reveals that variability models work best in centralized variability management scenarios, and that they are fragile and have to be controlled by a small team. We also identify a particular type of dependencies that is increasingly used in open platforms and helps sustain the growth of ecosystems. Interestingly, while enabling distributed variability, these dependencies rely on a centralized and stable vocabulary. Finally, we formulate new hypotheses and research questions that provide direction for future research.

Among the protected area system of Vietnam, Cat Ba appears as an ideal national park for biodiversity research and conservation. It covers a large area of karst landscape including islands and different ecosystems ranging from forests, wetland, mangroves, caves and others. Since the establishment of Cat Ba National Park in 1986, biodiversity research and conservation within the park have been strongly promoted and raised. The park has been well known as home to highly diverse flora and fauna with many species endemic to the archipelago and Vietnam. A series of projects and programmes have been effectively implemented for urgent and long-term conservation of threatened species. However, results from scientific research also indicated that many sites and species are still almost unstudied while several sections of the park’s buffer zone are affected by human activities including unscientific development of ecotourism. We recently conduct a field survey and recorded 2 bat species and echolocation calls in their natural habitats. This paper provides an overview of achievements with recent records and recommendations for strengthening conservation of biodiversity and habitats in the park and surroundings. / Trong hệ thống khu vực bảo vệ của Việt Nam, Cát Bà là một vườn quốc gia có điều kiện thuận lợi đối với công tác nghiên cứu và bảo tồn đa dạng sinh học. Vườn quốc gia này bao gồm diện tích lớn cảnh quan núi đá vôi với các đảo và hệ sinh thái đặc trưng như rừng trên núi, đất ngập nước, rừng ngập mặn, hang động và nhiều hệ sinh thái khác. Từ khi thành lập Vườn Quốc gia Cát Bà năm 1986, công tác nghiên cứu và bảo tồn đa dạng sinh học được quan tâm và thực hiện ngày càng nhiều. Vườn quốc gia cũng chứa đựng khu hệ động vật và thực vật đa dạng với nhiều loài đặc hữu cho quần đảo và Việt Nam. Nhiều dự án và chương trình đã được thực hiện nhằm bảo tồn cấp bách và lâu dài những loài bị đe dọa. Tuy nhiên, những kết quả nghiên cứu khoa học cũng cho thấy nhiều khu vực trong phạm vi vườn quốc gia gần như chưa được nghiên cứu trong khi một số tiểu khu thuộc vùng đệm đang bị ảnh hưởng bởi hoạt động của con người như sự phát triển du lịch. Chúng tôi đã ghi nhận được 2 loài dơi cùng với tiếng kêu siêu âm trong môi trường sống tự nhiên của chúng qua thời gian điều tra thực địa vừa qua. Bài báo này cung cấp dẫn liệu tổng quan và cập nhật về những kết quả đã đạt được với những thông tin cập nhật và đề xuất nhằm thúc đẩy công tác bảo tồn đa dạng sinh học và sinh cảnh ở vườn quốc gia và vùng phụ cận trong tương lai.

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The implementation of digital innovations in the healthcare sector is faced with different barriers and challenges. The complex system of regulations, the lack of interoperability, and highly dynamic interorganisational networks lead to missing widespread adoption of eHealth solutions. Digital platforms can help to overcome these barriers by providing a holistic infrastructure. They create a modularised foundation that innovators can use to create own innovations and provide them to demanders of digital solutions. As intermediaries, they can be accessed both by healthcare professionals and eHealth solution providers. Providers can offer their eHealth services via the platform. Healthcare professionals can use these services to create own interorganisational information systems. In the field of information systems research, effects and strategies for two-sided platforms are well researched and the potentials of eHealth platforms are also discussed. However, the organisational and technological design and methods for the construction of platforms are fewer questioned. Nonetheless, platform owners can benefit from implementation strategies and architectural guidance to create sustainable platforms and surrounding ecosystems. This doctoral thesis questions how domain-specific platforms can be designed systematically. Conducting a design-science research process, it develops both a modelling system and the Dresden Ecosystem Management Method (DREEM) to support the development of platforms in different domains. Furthermore, it describes the design characteristics of two-sided platforms in the healthcare sector and provides an evaluation approach to analyse the platforms’ ability to create a viable innovation ecosystem in the healthcare sector. The doctoral thesis contributes by providing methodical guidance for platform owners and researchers to design and evaluate digital platforms in different domains and improves the understanding of platform theory in the healthcare sector.:A. Synopsis of the Doctoral Thesis 1. Introduction 2. Foundational Considerations 3. Requirements for Design Artefacts and Knowledge 4. Structure of the Doctoral Thesis 5. Conclusion B. Paper 1 - Governance Guidelines for Digital Healthcare Ecosystems C. Paper 2 - Revise your eHealth Platform! D. Paper 3 - Business Model Open ”E-Health-Platform” E. Paper 4 - Modelling Ecosystems in Information Systems F. Paper 5 - Designing Industrial Symbiosis Platforms G. Paper 6 - Management of Digital Ecosystems with DREEM H. Paper 7 - Guiding the Development of Digital Ecosystems I. Paper 8 - Towards Maintenance Analytics Ecosystems J. Paper 9- Sustainability of E-Health-Projects K. Paper 10 - ISO 11354-2 for the Evaluation of eHealth-Platforms

Variability modeling is one of the key disciplines to cope with complex variability in large software product lines. It aims at creating, evolving, and configuring variability models, which describe the common and variable characteristics, also known as features, of products in a product line. Since the introduction of feature models more than twenty years ago, many variability modeling languages and notations have been proposed both in academia and industry, followed by hundreds of publications on variability modeling techniques that have built upon these theoretical foundations. Surprisingly, there are relatively few empirical studies that aim at understanding the use of such languages. What variability modeling concepts are actually used in practice? Do variability models applied in real-world look similar to those published in literature? In what technical and organizational contexts are variability models applicable? We present an empirical study that addresses this research gap. Our goals are i) to verify existing theoretical research, and ii) to explore real-world variability modeling languages and models expressed in them. We study concepts and semantics of variability modeling languages conceived by practitioners, and the usage of these concepts in real, large-scale models. Our aim is to support variability modeling research by providing empirical data about the use of its core modeling concepts, by identifying and characterizing further concepts that have not been as widely addressed, and by providing realistic assumptions about scale, structure, content, and complexity of real-world variability models. We believe that our findings are of relevance to variability modeling researchers and tool designers, for example, those working on interactive product configurators or feature dependency checkers. Our extracted models provide realistic benchmarks that can be used to evaluate new techniques. Recognizing the recent trend in software engineering to open up software platforms to facilitate inter-organizational reuse of software, we extend our empirical discourse to the emerging field of software ecosystems. As natural successors of successful product lines, ecosystems manage huge variability among and within their software assets, thus, represent a highly interesting class of systems to study variability modeling concepts and mechanisms. Our studied systems comprise eleven highly configurable software systems, two ecosystems with closed platforms, and three ecosystems relying on open platforms. Some of our subjects are among the largest successful systems in existence today. Results from a survey on industrial variability modeling complement these subjects. Our overall results provide empirical evidence that the well-researched concepts of feature modeling are used in practice, but also that more advanced concepts are needed. We observe that assumptions about variability models in the literature do not hold. Our study also reveals that variability models work best in centralized variability management scenarios, and that they are fragile and have to be controlled by a small team. We also identify a particular type of dependencies that is increasingly used in open platforms and helps sustain the growth of ecosystems. Interestingly, while enabling distributed variability, these dependencies rely on a centralized and stable vocabulary. Finally, we formulate new hypotheses and research questions that provide direction for future research.

Forest and forestland are important roles and sources of livelihood for the population living in or near forests and in mountainous areas of Vietnam. The objectives of this paper analysed the change in forest resource, and policy of forestry in Vietnam. In recent several years, forest area rapidly covered an average rate of 240,000 ha/year and had about 13.39 million hectares in 2010. It has contributed to the use of bare land, job creation and improvement of livelihoods for 25% of Vietnam’s population living in mountainous areas. Those results were the purpose of reforestation program and the production of wood industry in Vietnam. In this addition, government policies and regulations have provided a solid foundation for development of the forest plantations and conservation of forest ecosystems though forest land allocation and lease to organizations, households, and individuals. Therefore, the forest utilization has motivated by both environmental and commercial factors in Vietnam based on dividing into three forest categories special use, protection and production forests. However, the development strategy of forest management plan is the difficulties associated with conflicting land claims and boundary disputes due to the value of the established forest. / Rừng và đất rừng đóng vai trò quan trọng và là nguồn sinh kế cho người dân sống trong hoặc gần rừng ở các khu vực miền núi của Việt Nam. Mục tiêu của nghiên cứu này phân tích sự thay đổi về tài nguyên rừng và chính sách về lâm nghiệp. Trong một vài năm gần đây, diện tích rừng bao phủ nhanh với tốc độ trung bình 240.000 ha/năm và có khoảng 13,39 triệu ha trong năm 2010 này đã góp phần vào việc sử dụng đất trống, tạo việc làm và cải thiện đời sống cho 25% dân số sống ở khu vực miền núi của Việt Nam. Kết quả này là mục đích của chương trình trồng rừng và sản xuất gỗ công nghiệp tại Việt Nam. Bên cạnh đó, chính sách và các quy định của chính phủ đã cung cấp một nền tảng vững chắc cho việc phát triển diện tích trồng rừng và bảo tồn hệ sinh thái rừng mặc dù rừng và đất rừng đã được giao và khoán cho các tổ chức, hộ gia đình, cá nhân. Vì vậy, việc sử dụng rừng đã thúc đẩy bởi hai yếu tố môi trường và thương mại ở Việt Nam, dựa trên phân loại rừng: rừng đặc dụng, rừng sản xuất và rừng phòng hộ. Tuy nhiên, chiến lược kế hoạch quản lý phát triển rừng có những khó khăn liên quan đến xung đột khiếu nại đất và tranh chấp biên giới do giá trị của rừng được thành lập.

Over the years, data management has diversified and moved into multiple directions, mainly caused by a significant growth in the application space with different usage patterns, a massive change in the underlying hardware characteristics, and-last but not least-growing data volumes to be processed. A solution matching these constraints has to cope with a multidimensional problem space including techniques dealing with a large number of domain-specific data types, data and consistency models, deployment scenarios, and processing, storage, and communication infrastructures on a hardware level. Specialized database engines are available and are positioned in the market optimizing a particular dimension on the one hand while relaxing other aspects (e.g. web-scale deployment with relaxed consistency). Today it is common sense, that there is no single engine which can handle all the different dimensions equally well and therefore we have very good reasons to tackle this problem and optimize the dimensions with specialized approaches in a first step. However, we argue for a second step (reflecting in our opinion on the even harder problem) of a deep integration of individual engines into a single coherent and consistent data management ecosystem providing not only shared components but also a common understanding of the overall business semantics. More specifically, a data management ecosystem provides common “infrastructure” for software and data life cycle management, backup/recovery, replication and high availability, accounting and monitoring, and many other operational topics, where administrators and users expect a harmonized experience. More importantly from an application perspective however, customer experience teaches us to provide a consistent business view across all different components and the ability to seamlessly combine different capabilities. For example, within recent customer-based Internet of Things scenarios, a huge potential exists in combining graph-processing functionality with temporal and geospatial information and keywords extracted from high-throughput twitter streams. Using SAP HANA as the running example, we want to demonstrate what moving a set of individual engines and infra-structural components towards a holistic but also flexible data management ecosystem could look like. Although there are some solutions for some problems already visible on the horizon, we encourage the database research community in general to focus more on the Big Picture providing a holistic/integrated approach to efficiently deal with different types of data, with different access methods, and different consistency requirements-research in this field would push the envelope far beyond the traditional notion of data management.

Im digitalen Zeitalter sind Innovationskraft und eine effiziente Adaption digitaler Technologien für Unternehmen entscheidend, um sich Wettbewerbsvorteile zu sichern. Der Einsatz digitaler Technologien für Innovation verspricht in diesem Zusammenhang nicht nur Produktivitätsvorteile, sondern steigert auch die Kundenzufriedenheit und macht Unternehmen agiler und widerstandsfähiger gegenüber Krisen. Eine zentrale Rolle spielt dabei die Anwendung von Big Data Analytics, jedoch bestehen derzeit erhebliche Forschungsbedarfe, um genauer zu ergründen, wie Big Data Analytics systematisch in Innovationsökosystemen genutzt werden können. Zum einen herrscht ein Mangel an Forschung über die strategischen Beiträge von Big Data Analytics für Innovation, insbesondere im Kontext des Zusammenwirkens verschiedener Akteure. Zum anderen liegt der Fokus bestehender Forschungsarbeiten oft nur auf Teilaspekten der Anwendung von Big Data Analytics und vernachlässigt umfassendere Betrachtungen, aus einer Ökosystem-Perspektive heraus. Für die Praxis liegen die primären Hürden dabei häufig nicht in der Technologie selbst, sondern in deren Adaption innerhalb der wertschöpfenden Strukturen von Unternehmen. Diese Dissertation zielt darauf ab, diese Lücke zu schließen und untersucht die systematische Anwendung von Big Data Analytics in Innovationsökosystemen und nutzt dafür einen Design-Science-Research-Ansatz als übergeordnete Forschungsmethode. Im Dachbeitrag und in den Einzelbeiträgen des kumulativen Dissertationsvorhabens wird dafür gestaltungsorientierte Forschung angewendet, um theoretische Erkenntnisse direkt in die praktische Gestaltung und Entwicklung von Lösungen zu integrieren. Im Ergebnis liefert die Dissertation einen übergeordneten Ordnungsrahmen für die Anwendung von Big Data Analytics in Innovationsökosystemen, der die gesammelten Erkenntnisse aus dem Forschungsprojekt CODIFeY und den Einzelbeiträgen integriert. Damit trägt die Dissertation über den entwickelten Ordnungsrahmen und die IT-Artefakte der Einzelbeiträge dazu bei, ein besseres Verständnis für die strategische Nutzung digitaler Technologien zur Förderung von Innovation und Wettbewerbsvorteilen zu erreichen, was sowohl wissenschaftlich als auch praktisch einen Mehrwert bietet.:Danksagung i Einzelbeiträge iii Inhaltsverzeichnis iv Abkürzungsverzeichnis x Abbildungsverzeichnis xii Tabellenverzeichnis xiv Kurzzusammenfassung 1 Abstract 2 I. Dachbeitrag 3 1 Einleitung 3 1.1 Motivation 3 1.2 Problem- und Fragestellung 5 1.3 Zielstellung 8 1.4 Aufbau des Dachbeitrags 9 2 Forschungsansatz 11 2.1 Wissenschaftstheoretische Grundpositionierung 11 2.2 Forschungsmethode 12 2.2.1 Design Science Research als übergeordnetes Forschungsparadigma 12 2.2.2 Das Projekt Community-basierte Dienstleistungs-Innovation für e-Mobility 14 2.2.3 Aufbau des kumulativen Dissertationsvorhabens 17 3 Stand der Wissenschaft und Forschung 24 3.1 Big Data Analytics 24 3.2 Datengetriebene Innovation 25 3.3 Innovationsökosysteme aus der Perspektive der Service Dominant Logic 27 4 Gestaltung eines Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 30 4.1 Das Modell eines Innovationsökosystems aus Sicht der Service Dominant Logic 30 4.2 Ableitung der Dimensionen des Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 35 5 Eine Systematisierung von Anwendungsfällen von Big Data Analytics in Innovationsökosystemen 39 5.1 Big Data Analytics als Mittel für Innovation 39 5.2 Big Data Analytics als Ergebnis von Innovation 44 5.3 Demonstration & Evaluation des Ordnungsrahmens 50 6 Fazit 52 II. Research Papers of the Dissertation 55 Paper A – Capturing the Bigger Picture? Applying Text Analytics to Foster Open Innovation 55 A1 Introduction 57 A2 Background and Terminology 60 A2.1 Complexities of Sustainability-Oriented Innovation 60 A2.2 Open Innovation as an Instrument for Participation 62 A2.3 Sustainable-Oriented Innovation and Open Innovation 64 A2.4 Silent Stakeholders 67 A2.5 Research Focus: Text Analytics in Direct Search Methods for Sustainability-Oriented Innovation 69 A3 Action Research Study 72 A3.1 Description of the Action Research Cycle 72 A3.2 Diagnosing the Project Background 73 A3.3 Action Planning and Taking—Application of Text Analytics 77 A4 Results 82 A4.1 Findings from the Overall Discourse Analysis 82 A4.2 Findings from Zooming into Single Topics 84 A4.3 Applicability in the Innovation Process for the Label Development 85 A5 Discussion 87 A6 Implications and Conclusions 88 Paper B – What the Hack? – Towards a Taxonomy of Hackathons 92 B1 Introduction 93 B2 A Process-centric Perspective on Open Innovation and Hackathons 95 B3 Research Approach 97 B3.1 Taxonomy Development 97 B3.2 Literature Review 98 B4 A Taxonomy of Hackathons 101 B4.1 Overview of the Taxonomy 101 B4.2 Strategic Design Decisions 102 B4.3 Operational Design Decisions 104 B5 Discussion 107 B6 Conclusion 109 Paper C – Combining Open Innovation and Knowledge Management in Communities of Practice - An Analytics Driven Approach 110 C1 Introduction 111 C2 Foundations 113 C2.1 Knowledge Management and Innovation 113 C2.2 Communities of Practice 114 C2.3 Analytics domains 114 C3 Research Methodology 117 C4 Conceptual Framework for the Integration of Open Innovation and Knowledge Management 118 C4.1 Conceptual Data Model 119 C5 Implementation & Evaluation of a Pilot Project 122 C5.1 The Research Project CODIFeY 122 C5.2 Evaluation and Preliminary Findings 124 C6 Conclusions 126 Paper D – Entwicklung eines Analytics Framework für virtuelle Communities of Practice 127 D1 Einführung 128 D2 Grundlagen 130 D2.1 Communities of Practice 130 D2.2 Analytics 131 D2.3 Design eines Analytics Frameworks für Communities of Practice 132 D3 Demonstration und Evaluation im Projekt CODIFeY 136 D4 Fazit 138 Paper E – Teaching Data Driven Innovation – Facing a Challenge for Higher Education 139 E1 Introduction 140 E2 Foundations and Theoretical Underpinning 142 E2.1 Data Driven Innovation 142 E2.2 Teaching Data-Driven Innovation 142 E2.3 Pedagogical Approach 143 E3 Research Method 145 E3.1 General Morphological Analysis 145 E3.2 Data Collection and Empirical Analysis 146 E4 Design of the Morphological Box 148 E4.1 Teaching Method 148 E4.2 Course Setting 149 E4.3 Course Content 149 E4.4 Innovation Approach 150 E4.5 Morphological Box for Teaching Data Driven Innovation 151 E5 Teaching Cases 153 E5.1 Case A: Data Driven Value Generation for the Internet of Things 153 E5.2 Case B: Data Driven Innovation Project in the Field of E-mobility 154 E6 Conclusion 156 Paper F – Cross-Disciplinary Collaboration for Designing Data-Driven Products and Services 157 F1 Introduction 158 F2 Foundations and Theoretical Background 161 F2.1 Data Literacy as a Foundation for the Design of Data-Driven Product and Services 161 F2.2 Collaborative Processes and Knowledge Transfer 162 F2.3 Knowledge Boundaries 162 F2.4 Boundary Objects 163 F2.5 Boundary Objects for Collaboration Processes and Knowledge Integration 164 F3 Research Approach 166 F4 Design of the Data Vignette 169 F4.1 Thematic View 169 F4.2 Structural View 173 F5 Evaluation of the Artifact 178 F5.1 Artificial Evaluation Using the Guidelines of Modelling 178 F5.2 Application of the DV - A First Pilot 179 F6 Conclusion 182 Paper G – Towards the Development of a Typology of Big Data Analytics in Innovation Ecosystems 184 G1 Introduction 185 G2 Foundations 187 G2.1 The Role of Technology for Innovation Ecosystems 187 G2.2 Big Data Analytics in Innovation Ecosystems 188 G3 Research Approach 189 G4 Towards a Typology of Big Data Analytics in Innovation Ecosystems 190 G5 Further research 192 Paper H – Hackathons als Gestaltungswerkzeug für plattform-basierte digitale Ökosysteme 193 H1 Einleitung 194 H2 Grundlagen 196 H2.1 Plattform-basierte digitale Ökosysteme 196 H2.2 Hackathons als Gestaltungswerkzeug 197 H3 Forschungsmethode 199 H4 Hackathons für die Gestaltung plattform-basierter Ökosysteme 202 H4.1 Markt-orientierte Plattform-Hackathons 202 H4.2 Technologie-orientierte Plattform-Hackathons 204 H5 Fazit 206 Literaturverzeichnis xv Anhang li Anhang 1 li / In the digital age, the ability to innovate and the efficient adoption of digital technologies are crucial for companies to gain competitive advantages. The use of digital technologies for innovation promises not only productivity gains but also increases customer satisfaction and makes companies more agile and resilient to crises. The focus here is on the application of big data analytics, but there is currently still a considerable need for research to understand how big data analytics can be used systematically in innovation ecosystems. On the one hand, there is a lack of research on the strategic contributions of big data analytics to innovation, particularly in the context of the interaction of various actors. On the other hand, the focus of existing research often only addresses partial aspects of the application of big data analytics and neglects broader considerations from an ecosystem perspective. For practice, the primary hurdles often lie not in the technology itself but in its adaptation within the value-creating structures of companies. This dissertation aims to close this gap and examines the systematic application of big data analytics in innovation ecosystems, using a design science research approach as the overarching research method. In the summary and in the individual papers of the cumulative dissertation project, design-oriented research is used to integrate theoretical insights directly into the practical design and development of solutions. As a result, the dissertation provides an overarching framework for the application of big data analytics in innovation ecosystems, integrating the insights gathered from the CODIFeY research project and the individual contributions. The dissertation on the developed framework and the IT artifacts of the individual contributions contributes to a better understanding of the strategic use of digital technologies to promote innovation and competitive advantages, which offers added value both scientifically and practically.:Danksagung i Einzelbeiträge iii Inhaltsverzeichnis iv Abkürzungsverzeichnis x Abbildungsverzeichnis xii Tabellenverzeichnis xiv Kurzzusammenfassung 1 Abstract 2 I. Dachbeitrag 3 1 Einleitung 3 1.1 Motivation 3 1.2 Problem- und Fragestellung 5 1.3 Zielstellung 8 1.4 Aufbau des Dachbeitrags 9 2 Forschungsansatz 11 2.1 Wissenschaftstheoretische Grundpositionierung 11 2.2 Forschungsmethode 12 2.2.1 Design Science Research als übergeordnetes Forschungsparadigma 12 2.2.2 Das Projekt Community-basierte Dienstleistungs-Innovation für e-Mobility 14 2.2.3 Aufbau des kumulativen Dissertationsvorhabens 17 3 Stand der Wissenschaft und Forschung 24 3.1 Big Data Analytics 24 3.2 Datengetriebene Innovation 25 3.3 Innovationsökosysteme aus der Perspektive der Service Dominant Logic 27 4 Gestaltung eines Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 30 4.1 Das Modell eines Innovationsökosystems aus Sicht der Service Dominant Logic 30 4.2 Ableitung der Dimensionen des Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 35 5 Eine Systematisierung von Anwendungsfällen von Big Data Analytics in Innovationsökosystemen 39 5.1 Big Data Analytics als Mittel für Innovation 39 5.2 Big Data Analytics als Ergebnis von Innovation 44 5.3 Demonstration & Evaluation des Ordnungsrahmens 50 6 Fazit 52 II. Research Papers of the Dissertation 55 Paper A – Capturing the Bigger Picture? Applying Text Analytics to Foster Open Innovation 55 A1 Introduction 57 A2 Background and Terminology 60 A2.1 Complexities of Sustainability-Oriented Innovation 60 A2.2 Open Innovation as an Instrument for Participation 62 A2.3 Sustainable-Oriented Innovation and Open Innovation 64 A2.4 Silent Stakeholders 67 A2.5 Research Focus: Text Analytics in Direct Search Methods for Sustainability-Oriented Innovation 69 A3 Action Research Study 72 A3.1 Description of the Action Research Cycle 72 A3.2 Diagnosing the Project Background 73 A3.3 Action Planning and Taking—Application of Text Analytics 77 A4 Results 82 A4.1 Findings from the Overall Discourse Analysis 82 A4.2 Findings from Zooming into Single Topics 84 A4.3 Applicability in the Innovation Process for the Label Development 85 A5 Discussion 87 A6 Implications and Conclusions 88 Paper B – What the Hack? – Towards a Taxonomy of Hackathons 92 B1 Introduction 93 B2 A Process-centric Perspective on Open Innovation and Hackathons 95 B3 Research Approach 97 B3.1 Taxonomy Development 97 B3.2 Literature Review 98 B4 A Taxonomy of Hackathons 101 B4.1 Overview of the Taxonomy 101 B4.2 Strategic Design Decisions 102 B4.3 Operational Design Decisions 104 B5 Discussion 107 B6 Conclusion 109 Paper C – Combining Open Innovation and Knowledge Management in Communities of Practice - An Analytics Driven Approach 110 C1 Introduction 111 C2 Foundations 113 C2.1 Knowledge Management and Innovation 113 C2.2 Communities of Practice 114 C2.3 Analytics domains 114 C3 Research Methodology 117 C4 Conceptual Framework for the Integration of Open Innovation and Knowledge Management 118 C4.1 Conceptual Data Model 119 C5 Implementation & Evaluation of a Pilot Project 122 C5.1 The Research Project CODIFeY 122 C5.2 Evaluation and Preliminary Findings 124 C6 Conclusions 126 Paper D – Entwicklung eines Analytics Framework für virtuelle Communities of Practice 127 D1 Einführung 128 D2 Grundlagen 130 D2.1 Communities of Practice 130 D2.2 Analytics 131 D2.3 Design eines Analytics Frameworks für Communities of Practice 132 D3 Demonstration und Evaluation im Projekt CODIFeY 136 D4 Fazit 138 Paper E – Teaching Data Driven Innovation – Facing a Challenge for Higher Education 139 E1 Introduction 140 E2 Foundations and Theoretical Underpinning 142 E2.1 Data Driven Innovation 142 E2.2 Teaching Data-Driven Innovation 142 E2.3 Pedagogical Approach 143 E3 Research Method 145 E3.1 General Morphological Analysis 145 E3.2 Data Collection and Empirical Analysis 146 E4 Design of the Morphological Box 148 E4.1 Teaching Method 148 E4.2 Course Setting 149 E4.3 Course Content 149 E4.4 Innovation Approach 150 E4.5 Morphological Box for Teaching Data Driven Innovation 151 E5 Teaching Cases 153 E5.1 Case A: Data Driven Value Generation for the Internet of Things 153 E5.2 Case B: Data Driven Innovation Project in the Field of E-mobility 154 E6 Conclusion 156 Paper F – Cross-Disciplinary Collaboration for Designing Data-Driven Products and Services 157 F1 Introduction 158 F2 Foundations and Theoretical Background 161 F2.1 Data Literacy as a Foundation for the Design of Data-Driven Product and Services 161 F2.2 Collaborative Processes and Knowledge Transfer 162 F2.3 Knowledge Boundaries 162 F2.4 Boundary Objects 163 F2.5 Boundary Objects for Collaboration Processes and Knowledge Integration 164 F3 Research Approach 166 F4 Design of the Data Vignette 169 F4.1 Thematic View 169 F4.2 Structural View 173 F5 Evaluation of the Artifact 178 F5.1 Artificial Evaluation Using the Guidelines of Modelling 178 F5.2 Application of the DV - A First Pilot 179 F6 Conclusion 182 Paper G – Towards the Development of a Typology of Big Data Analytics in Innovation Ecosystems 184 G1 Introduction 185 G2 Foundations 187 G2.1 The Role of Technology for Innovation Ecosystems 187 G2.2 Big Data Analytics in Innovation Ecosystems 188 G3 Research Approach 189 G4 Towards a Typology of Big Data Analytics in Innovation Ecosystems 190 G5 Further research 192 Paper H – Hackathons als Gestaltungswerkzeug für plattform-basierte digitale Ökosysteme 193 H1 Einleitung 194 H2 Grundlagen 196 H2.1 Plattform-basierte digitale Ökosysteme 196 H2.2 Hackathons als Gestaltungswerkzeug 197 H3 Forschungsmethode 199 H4 Hackathons für die Gestaltung plattform-basierter Ökosysteme 202 H4.1 Markt-orientierte Plattform-Hackathons 202 H4.2 Technologie-orientierte Plattform-Hackathons 204 H5 Fazit 206 Literaturverzeichnis xv Anhang li Anhang 1 li